feat(notmnist): notmnist example using SGD + learning rate decay
This commit is contained in:
Mahdi Dibaiee
parent
ace0a18653
commit
b26347e19f
3
.gitmodules
vendored
3
.gitmodules
vendored
@ -1,3 +1,6 @@
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[submodule "examples/doc-classifier-data"]
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path = examples/doc-classifier-data
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url = git@github.com:mdibaiee/doc-classifier-data
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[submodule "examples/notMNIST"]
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path = examples/notMNIST
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url = git@github.com:mdibaiee/notMNIST
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49
examples/424encoder.hs
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49
examples/424encoder.hs
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@ -0,0 +1,49 @@
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module Main where
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import Sibe
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import Numeric.LinearAlgebra
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import Data.List
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import Debug.Trace
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import Data.Default.Class
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main = do
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let alpha = 0.5
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epochs = 1000
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a = (sigmoid, sigmoid')
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rnetwork = randomNetwork 0 (-0.1, 0.1) 4 [(2, a)] (4, a)
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inputs = [vector [1, 0, 0, 0],
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vector [0, 1, 0, 0],
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vector [0, 0, 1, 0],
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vector [0, 0, 0, 1]]
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labels = [vector [1, 0, 0, 0],
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vector [0, 1, 0, 0],
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vector [0, 0, 1, 0],
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vector [0, 0, 0, 1]]
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session = def { network = rnetwork
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, learningRate = 0.5
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, epochs = 1000
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, training = zip inputs labels
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, test = zip inputs labels
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} :: Session
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let initialCost = crossEntropy session
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newsession <- run gd session
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let results = map (`forward` newsession) inputs
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rounded = map (map round . toList) results
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cost = crossEntropy newsession
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putStrLn "parameters: "
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putStrLn $ "- inputs: " ++ show inputs
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putStrLn $ "- labels: " ++ show labels
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putStrLn $ "- learning rate: " ++ show alpha
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putStrLn $ "- epochs: " ++ show epochs
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putStrLn $ "- initial cost (cross-entropy): " ++ show initialCost
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putStrLn "results: "
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putStrLn $ "- actual result: " ++ show results
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putStrLn $ "- rounded result: " ++ show rounded
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putStrLn $ "- cost (cross-entropy): " ++ show cost
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@ -1,6 +1,7 @@
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module Main
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where
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-- import Sibe
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import Sibe.NLP
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import Sibe.NaiveBayes
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import Text.Printf
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import Data.List
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@ -28,14 +29,14 @@ module Main
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documents = cleanDocuments . removeWords sws $ createDocuments classes dataset
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testDocuments = cleanDocuments $ createDocuments classes test
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nb = train documents intClasses
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nb = initialize documents intClasses
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-- top-ten
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topClasses = take 10 . reverse $ sortBy (compare `on` (length . snd)) (cd nb)
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filtered = map (\(c, ds) -> (c, take 100 ds)) topClasses
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filteredClasses = map fst filtered
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ttDocs = concatMap snd filtered
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ttNB = train ttDocs filteredClasses
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ttNB = initialize ttDocs filteredClasses
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ttTestDocuments = filter ((`elem` filteredClasses) . c) . cleanDocuments $ createDocuments classes test
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@ -1,54 +0,0 @@
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{-# LANGUAGE BangPatterns #-}
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module Main
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where
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-- import Sibe
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import Sibe.NaiveBayes
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import Text.Printf
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import Data.List
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import Data.Maybe
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import Debug.Trace
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import Data.List.Split
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import System.Directory
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import Control.DeepSeq
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import System.IO
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main = do
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putStr "Reading documents... "
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neg_documents <- createDocuments "examples/sentiment-analysis-data/train/neg/"
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pos_documents <- createDocuments "examples/sentiment-analysis-data/train/pos/"
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test_neg_documents <- createDocuments "examples/sentiment-analysis-data/test/neg/"
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test_pos_documents <- createDocuments "examples/sentiment-analysis-data/test/pos/"
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putStrLn "done"
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let classes = [0..9] -- rating, from 0 to 9 (1 to 10)
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documents = neg_documents ++ pos_documents
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nb = train documents classes
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testDocuments = neg_documents ++ pos_documents
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results = map (\(Document text c) -> (c, run text nb)) testDocuments
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-- results = map (\(Document text c) -> (c, determine text nb intClasses documents)) devTestDocuments
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print results
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-- let showResults (c, r) = putStrLn (show (classes !! c) ++ " ~ " ++ show (classes !! r))
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-- mapM_ showResults results
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--
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-- putStrLn $ "Recall: " ++ show (recall results)
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-- putStrLn $ "Precision: " ++ show (precision results)
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-- putStrLn $ "F Measure: " ++ show (fmeasure results)
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-- putStrLn $ "Accuracy: " ++ show (accuracy results)
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createDocuments :: FilePath -> IO [Document]
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createDocuments path = do
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files <- drop 2 <$> getDirectoryContents path
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let ratings = map (subtract 1 . read . take 1 . last . splitOn "_") files :: [Int]
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contents <- mapM (forceReadFile . (path ++)) files
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return $ zipWith Document contents ratings
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forceReadFile :: FilePath -> IO String
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forceReadFile file = do
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handle <- openFile file ReadMode
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content <- hGetContents handle
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content `deepseq` hClose handle
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return content
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1
examples/notMNIST
Submodule
1
examples/notMNIST
Submodule
@ -0,0 +1 @@
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Subproject commit 0dbdfd43ffb8e90a3657ed040fd1fb3d25654b51
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111
examples/notmnist.hs
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111
examples/notmnist.hs
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{-# LANGUAGE RecordWildCards #-}
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{-# LANGUAGE FlexibleContexts #-}
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{-# LANGUAGE ScopedTypeVariables #-}
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module Main where
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import Sibe
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import Numeric.LinearAlgebra
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import Data.List
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import Debug.Trace
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import System.IO
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import System.Directory
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import Codec.Picture
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import Codec.Picture.Types
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import qualified Data.Vector.Storable as V
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import Data.Either
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import System.Random
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import System.Random.Shuffle
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import Data.Default.Class
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import qualified Graphics.Rendering.Chart.Easy as Chart
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import Graphics.Rendering.Chart.Backend.Cairo
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main = do
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setStdGen (mkStdGen 100)
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let a = (sigmoid, sigmoid')
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o = (softmax, one)
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rnetwork = randomNetwork 0 (-1, 1) (28*28) [(100, a)] (10, a)
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(inputs, labels) <- dataset
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let trp = length inputs * 70 `div` 100
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tep = length inputs * 30 `div` 100
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-- training data
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trinputs = take trp inputs
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trlabels = take trp labels
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-- test data
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teinputs = take tep . drop trp $ inputs
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telabels = take tep . drop trp $ labels
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let session = def { learningRate = 0.5
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, batchSize = 32
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, epochs = 35
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, network = rnetwork
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, training = zip trinputs trlabels
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, test = zip teinputs telabels
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} :: Session
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let initialCost = crossEntropy session
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newsession <- run (sgd . learningRateDecay (1.1, 5e-2)) session
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let el = map (\(e, l, _) -> (e, l)) (chart newsession)
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ea = map (\(e, _, a) -> (e, a)) (chart newsession)
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toFile Chart.def "notmnist.png" $ do
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Chart.layoutlr_title Chart..= "loss over time"
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Chart.plotLeft (Chart.line "loss" [el])
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Chart.plotRight (Chart.line "learningRate" [ea])
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let cost = crossEntropy newsession
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putStrLn "parameters: "
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putStrLn $ "- batch size: " ++ show (batchSize session)
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putStrLn $ "- learning rate: " ++ show (learningRate session)
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putStrLn $ "- epochs: " ++ show (epochs session)
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putStrLn $ "- initial cost (cross-entropy): " ++ show initialCost
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putStrLn "results: "
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putStrLn $ "- accuracy: " ++ show (accuracy newsession)
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putStrLn $ "- cost (cross-entropy): " ++ show cost
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dataset :: IO ([Vector Double], [Vector Double])
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dataset = do
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let dir = "examples/notMNIST/"
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groups <- filter ((/= '.') . head) <$> listDirectory dir
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inputFiles <- mapM (listDirectory . (dir ++)) groups
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let n = 512 {-- minimum (map length inputFiles) --}
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numbers = map (`div` n) [0..n * length groups - 1]
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inputFilesFull = map (\(i, g) -> map ((dir ++ i ++ "/") ++) g) (zip groups inputFiles)
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inputImages <- mapM (mapM readImage . take n) inputFilesFull
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let names = map (take n) inputFilesFull
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let (l, r) = partitionEithers $ concat inputImages
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inputs = map (fromPixels . convertRGB8) r
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labels = map (\i -> V.replicate i 0 `V.snoc` 1 V.++ V.replicate (9 - i) 0) numbers
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pairs = zip inputs labels
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shuffled <- shuffleM pairs
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return (map fst shuffled, map snd shuffled)
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where
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fromPixels :: Image PixelRGB8 -> Vector Double
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fromPixels img@Image { .. } =
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let pairs = [(x, y) | x <- [0..imageWidth - 1], y <- [0..imageHeight - 1]]
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in V.fromList $ map iter pairs
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where
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iter (x, y) =
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let (PixelRGB8 r g b) = convertPixel $ pixelAt img x y
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in
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if r == 0 && g == 0 && b == 0 then 0 else 1
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0
examples/recurrent-doc-classifier.hs
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0
examples/recurrent-doc-classifier.hs
Normal file
@ -1,5 +0,0 @@
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XSym
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0040
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3666c4cacaf995ebd11ef25aab70de99
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../../sibe-repos/sentiment-analysis-data
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35
examples/sin.hs
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35
examples/sin.hs
Normal file
@ -0,0 +1,35 @@
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module Main where
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import Sibe
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import Numeric.LinearAlgebra
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import Data.List
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import Debug.Trace
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main = do
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let alpha = 0.5
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epochs = 1000
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a = (sigmoid, sigmoid')
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lo = (sigmoid, (\_ -> 1)) -- cross entropy
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-- a = (relu, relu')
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rnetwork = randomNetwork 0 (-1, 1) 1 [(50, a)] (1, lo)
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inputs = map (\a -> vector [a]) (reverse [0, 30, 45, 60, 90])
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labels = map (\deg -> vector $ [sin $ deg * pi/180]) (reverse [0, 30, 45, 60, 90])
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initial_cost = zipWith crossEntropy (map (`forward` rnetwork) inputs) labels
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network <- run session inputs rnetwork labels alpha epochs
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let results = map (`forward` network) inputs
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rounded = map (map round . toList) results
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cost = zipWith crossEntropy (map (`forward` network) inputs) labels
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putStrLn "parameters: "
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putStrLn $ "- inputs: " ++ show inputs
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putStrLn $ "- labels: " ++ show labels
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putStrLn $ "- learning rate: " ++ show alpha
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putStrLn $ "- epochs: " ++ show epochs
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{-putStrLn $ "- initial cost (cross-entropy): " ++ show initial_cost-}
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putStrLn "results: "
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putStrLn $ "- actual result: " ++ show results
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{-putStrLn $ "- cost (cross-entropy): " ++ show cost-}
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@ -3,30 +3,37 @@ module Main where
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import Numeric.LinearAlgebra
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import Data.List
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import Debug.Trace
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import Data.Default.Class
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main = do
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let learning_rate = 0.5
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(iterations, epochs) = (2, 1000)
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a = (sigmoid, sigmoid')
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rnetwork = randomNetwork 0 2 [(8, a)] (1, a) -- two inputs, 8 nodes in a single hidden layer, 1 output
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let a = (sigmoid, sigmoid')
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rnetwork = randomNetwork 0 (-1, 1) 2 [(2, a)] (1, a) -- two inputs, 8 nodes in a single hidden layer, 1 output
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inputs = [vector [0, 1], vector [1, 0], vector [1, 1], vector [0, 0]]
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labels = [vector [1], vector [1], vector [0], vector [0]]
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initial_cost = zipWith crossEntropy (map (`forward` rnetwork) inputs) labels
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session = def { network = rnetwork
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, learningRate = 0.5
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, epochs = 1000
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, training = zip inputs labels
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, test = zip inputs labels
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} :: Session
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network = session inputs rnetwork labels learning_rate (iterations, epochs)
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results = map (`forward` network) inputs
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initialCost = crossEntropy session
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newsession <- run gd session
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let results = map (`forward` newsession) inputs
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rounded = map (map round . toList) results
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cost = zipWith crossEntropy (map (`forward` network) inputs) labels
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cost = crossEntropy newsession
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putStrLn "parameters: "
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putStrLn $ "- inputs: " ++ show inputs
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putStrLn $ "- labels: " ++ show labels
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putStrLn $ "- learning rate: " ++ show learning_rate
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putStrLn $ "- iterations/epochs: " ++ show (iterations, epochs)
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putStrLn $ "- initial cost (cross-entropy): " ++ show initial_cost
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putStrLn $ "- learning rate: " ++ show (learningRate session)
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putStrLn $ "- epochs: " ++ show (epochs session)
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putStrLn $ "- initial cost (cross-entropy): " ++ show initialCost
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putStrLn "results: "
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putStrLn $ "- actual result: " ++ show results
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putStrLn $ "- rounded result: " ++ show rounded
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BIN
notmnist-0.png
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notmnist-0.png
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notmnist-1.png
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notmnist-2.png
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notmnist.png
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73
sibe.cabal
73
sibe.cabal
@ -15,7 +15,7 @@ cabal-version: >=1.10
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library
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hs-source-dirs: src
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exposed-modules: Sibe, Sibe.NaiveBayes
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exposed-modules: Sibe, Sibe.NaiveBayes, Sibe.NLP
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build-depends: base >= 4.7 && < 5
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, hmatrix
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, random
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@ -26,16 +26,21 @@ library
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, regex-pcre
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, text
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, stemmer
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, vector
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, random-shuffle
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, data-default-class
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, Chart
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, Chart-cairo
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default-language: Haskell2010
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executable sibe-exe
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hs-source-dirs: app
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main-is: Main.hs
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ghc-options: -threaded -rtsopts -with-rtsopts=-N
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build-depends: base
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, sibe
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, hmatrix
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default-language: Haskell2010
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--executable sibe-exe
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--hs-source-dirs: app
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--main-is: Main.hs
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--ghc-options: -threaded -rtsopts -with-rtsopts=-N
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--build-depends: base
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--, sibe
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--, hmatrix
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--default-language: Haskell2010
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executable example-xor
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hs-source-dirs: examples
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@ -44,6 +49,43 @@ executable example-xor
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build-depends: base
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, sibe
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, hmatrix
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, data-default-class
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default-language: Haskell2010
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--executable example-sin
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--hs-source-dirs: examples
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--main-is: sin.hs
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--ghc-options: -threaded -rtsopts -with-rtsopts=-N
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--build-depends: base
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--, sibe
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--, hmatrix
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--default-language: Haskell2010
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executable example-424
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hs-source-dirs: examples
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main-is: 424encoder.hs
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ghc-options: -threaded -rtsopts -with-rtsopts=-N
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build-depends: base
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, sibe
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, hmatrix
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, data-default-class
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default-language: Haskell2010
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executable example-notmnist
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hs-source-dirs: examples
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main-is: notmnist.hs
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ghc-options: -threaded -rtsopts -with-rtsopts=-N
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build-depends: base
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, sibe
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, hmatrix
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, directory >= 1.2.5.0
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, JuicyPixels == 3.2.7.2
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, vector == 0.11.0.0
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, random
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, random-shuffle
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, data-default-class
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, Chart
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, Chart-cairo
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default-language: Haskell2010
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executable example-naivebayes-doc-classifier
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@ -57,19 +99,6 @@ executable example-naivebayes-doc-classifier
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, split
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default-language: Haskell2010
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executable example-naivebayes-sentiment-analysis
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hs-source-dirs: examples
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main-is: naivebayes-sentiment-analysis.hs
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ghc-options: -threaded -rtsopts -with-rtsopts=-N
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build-depends: base
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, sibe
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, hmatrix
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, containers
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, split
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, directory
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, deepseq
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default-language: Haskell2010
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test-suite sibe-test
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type: exitcode-stdio-1.0
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hs-source-dirs: test
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258
src/Sibe.hs
258
src/Sibe.hs
@ -15,22 +15,37 @@ module Sibe
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saveNetwork,
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loadNetwork,
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train,
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session,
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shuffle,
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gd,
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sgd,
|
||||
run,
|
||||
sigmoid,
|
||||
sigmoid',
|
||||
softmax,
|
||||
softmax',
|
||||
one,
|
||||
relu,
|
||||
relu',
|
||||
crossEntropy,
|
||||
genSeed,
|
||||
replaceVector
|
||||
replaceVector,
|
||||
Session(..),
|
||||
accuracy,
|
||||
learningRateDecay
|
||||
) where
|
||||
import Numeric.LinearAlgebra
|
||||
import System.Random
|
||||
import System.Random.Shuffle
|
||||
import Debug.Trace
|
||||
import Data.List (foldl', sortBy)
|
||||
import Data.List (foldl', sortBy, genericLength, permutations)
|
||||
import System.IO
|
||||
import Control.DeepSeq
|
||||
import Control.Monad
|
||||
import qualified Data.Vector.Storable as V
|
||||
import Data.Default.Class
|
||||
import System.Exit
|
||||
|
||||
import qualified Graphics.Rendering.Chart.Easy as Chart
|
||||
import Graphics.Rendering.Chart.Backend.Cairo
|
||||
|
||||
type LearningRate = Double
|
||||
type Input = Vector Double
|
||||
@ -48,8 +63,33 @@ module Sibe
|
||||
data Network = O Layer
|
||||
| Layer :- Network
|
||||
deriving (Show)
|
||||
|
||||
infixr 5 :-
|
||||
|
||||
data Session = Session { network :: Network
|
||||
, training :: [(Vector Double, Vector Double)]
|
||||
, test :: [(Vector Double, Vector Double)]
|
||||
, learningRate :: Double
|
||||
, epochs :: Int
|
||||
, epoch :: Int
|
||||
, batchSize :: Int
|
||||
, chart :: [(Int, Double, Double)]
|
||||
, momentum :: Double
|
||||
}
|
||||
|
||||
emptyNetwork = randomNetwork 0 (0, 0) 0 [] (0, (id, id))
|
||||
instance Default Session where
|
||||
def = Session { network = seq (die "You have not specified a network parameter") emptyNetwork
|
||||
, training = seq (die "You have not specified training data") []
|
||||
, test = seq (die "You have not specified test data") []
|
||||
, learningRate = 0.5
|
||||
, epochs = 35
|
||||
, epoch = 0
|
||||
, batchSize = 0
|
||||
, chart = []
|
||||
, momentum = 0
|
||||
}
|
||||
|
||||
saveNetwork :: Network -> String -> IO ()
|
||||
saveNetwork network file =
|
||||
writeFile file ((show . reverse) (gen network []))
|
||||
@ -73,22 +113,24 @@ module Sibe
|
||||
runLayer :: Input -> Layer -> Output
|
||||
runLayer input (Layer !biases !weights _) = input <# weights + biases
|
||||
|
||||
forward :: Input -> Network -> Output
|
||||
forward input (O l@(Layer _ _ (fn, _))) = fn $ runLayer input l
|
||||
forward input (l@(Layer _ _ (fn, _)) :- n) = forward ((fst . activation $ l) $ runLayer input l) n
|
||||
forward :: Input -> Session -> Output
|
||||
forward input session = compute input (network session)
|
||||
where
|
||||
compute input (O l@(Layer _ _ (fn, _))) = fn $ runLayer input l
|
||||
compute input (l@(Layer _ _ (fn, _)) :- n) = compute ((fst . activation $ l) $ runLayer input l) n
|
||||
|
||||
randomLayer :: Seed -> (Int, Int) -> Activation -> Layer
|
||||
randomLayer seed (wr, wc) =
|
||||
let weights = uniformSample seed wr $ replicate wc (-1, 1)
|
||||
biases = randomVector seed Uniform wc * 2 - 1
|
||||
randomLayer :: Seed -> (Int, Int) -> (Double, Double) -> Activation -> Layer
|
||||
randomLayer seed (wr, wc) (l, u) =
|
||||
let weights = uniformSample seed wr $ replicate wc (l, u)
|
||||
biases = randomVector seed Uniform wc * realToFrac u - realToFrac l
|
||||
in Layer biases weights
|
||||
|
||||
randomNetwork :: Seed -> Int -> [(Int, Activation)] -> (Int, Activation) -> Network
|
||||
randomNetwork seed input [] (output, a) =
|
||||
O $ randomLayer seed (input, output) a
|
||||
randomNetwork seed input ((h, a):hs) output =
|
||||
randomLayer seed (input, h) a :-
|
||||
randomNetwork (seed + 1) h hs output
|
||||
randomNetwork :: Seed -> (Double, Double) -> Int -> [(Int, Activation)] -> (Int, Activation) -> Network
|
||||
randomNetwork seed bound input [] (output, a) =
|
||||
O $ randomLayer seed (input, output) bound a
|
||||
randomNetwork seed bound input ((h, a):hs) output =
|
||||
randomLayer seed (input, h) bound a :-
|
||||
randomNetwork (seed + 1) bound h hs output
|
||||
|
||||
sigmoid :: Vector Double -> Vector Double
|
||||
sigmoid x = 1 / max (1 + exp (-x)) 1e-10
|
||||
@ -96,18 +138,37 @@ module Sibe
|
||||
sigmoid' :: Vector Double -> Vector Double
|
||||
sigmoid' x = sigmoid x * (1 - sigmoid x)
|
||||
|
||||
softmax :: Vector Double -> Vector Double
|
||||
softmax x = cmap (\a -> exp a / s) x
|
||||
where
|
||||
s = V.sum $ exp x
|
||||
|
||||
one :: a -> Double
|
||||
one x = 1
|
||||
|
||||
softmax' :: Vector Double -> Vector Double
|
||||
softmax' x = softmax x * (1 - softmax x)
|
||||
|
||||
relu :: Vector Double -> Vector Double
|
||||
relu x = log (max (1 + exp x) 1e-10)
|
||||
relu = cmap (max 0.1)
|
||||
|
||||
relu' :: Vector Double -> Vector Double
|
||||
relu' = sigmoid
|
||||
relu' = cmap dev
|
||||
where dev x
|
||||
| x < 0 = 0
|
||||
| otherwise = 1
|
||||
|
||||
crossEntropy :: Output -> Output -> Double
|
||||
crossEntropy output target =
|
||||
let pairs = zip (toList output) (toList target)
|
||||
n = fromIntegral (length pairs)
|
||||
in (-1 / n) * sum (map f pairs)
|
||||
crossEntropy :: Session -> Double
|
||||
crossEntropy session =
|
||||
let inputs = map fst (test session)
|
||||
labels = map (toList . snd) (test session)
|
||||
outputs = map (toList . (`forward` session)) inputs
|
||||
pairs = zip outputs labels
|
||||
n = genericLength pairs
|
||||
|
||||
in sum (map set pairs) / n
|
||||
where
|
||||
set (os, ls) = (-1 / genericLength os) * sum (zipWith (curry f) os ls)
|
||||
f (a, y) = y * log (max 1e-10 a) + (1 - y) * log (max (1 - a) 1e-10)
|
||||
|
||||
train :: Input
|
||||
@ -138,35 +199,137 @@ module Sibe
|
||||
o = fn y
|
||||
(n', delta) = run o n
|
||||
|
||||
de = delta * fn' y -- quadratic cost
|
||||
de = delta * fn' y
|
||||
|
||||
biases' = biases - scale alpha de
|
||||
weights' = weights - scale alpha (input `outer` de)
|
||||
biases' = biases - cmap (*alpha) de
|
||||
weights' = weights - cmap (*alpha) (input `outer` de)
|
||||
layer = Layer biases' weights' (fn, fn')
|
||||
|
||||
pass = weights #> de
|
||||
-- pass = weights #> de
|
||||
in (layer :- n', pass)
|
||||
|
||||
session :: [Input] -> Network -> [Output] -> Double -> (Int, Int) -> Network
|
||||
session inputs network labels alpha (iterations, epochs) =
|
||||
let n = length inputs
|
||||
indexes = shuffle n (map (`mod` n) [0..n * epochs])
|
||||
in foldl' iter network indexes
|
||||
{-trainMomentum :: Input
|
||||
-> Network
|
||||
-> Output -- target
|
||||
-> Double -- learning rate
|
||||
-> (Double, Double) -- momentum
|
||||
-> Network -- network's output
|
||||
trainMomentum input network target alpha (m, v) = fst $ run input network
|
||||
where
|
||||
iter net i =
|
||||
let n = length inputs
|
||||
index = i `mod` n
|
||||
input = inputs !! index
|
||||
label = labels !! index
|
||||
in foldl' (\net _ -> train input net label alpha) net [0..iterations]
|
||||
run :: Input -> Network -> (Network, Vector Double)
|
||||
run input (O l@(Layer biases weights (fn, fn'))) =
|
||||
let y = runLayer input l
|
||||
o = fn y
|
||||
delta = o - target
|
||||
de = delta * fn' y
|
||||
v =
|
||||
-- de = delta -- cross entropy cost
|
||||
|
||||
shuffle :: Seed -> [a] -> [a]
|
||||
shuffle seed list =
|
||||
let ords = map ord $ take (length list) (randomRs (0, 1) (mkStdGen seed) :: [Int])
|
||||
in map snd $ sortBy (\x y -> fst x) (zip ords list)
|
||||
where ord x | x == 0 = LT
|
||||
| x == 1 = GT
|
||||
biases' = biases - scale alpha de
|
||||
weights' = weights - scale alpha (input `outer` de) -- small inputs learn slowly
|
||||
layer = Layer biases' weights' (fn, fn') -- updated layer
|
||||
|
||||
pass = weights #> de
|
||||
-- pass = weights #> de
|
||||
|
||||
in (O layer, pass)
|
||||
run input (l@(Layer biases weights (fn, fn')) :- n) =
|
||||
let y = runLayer input l
|
||||
o = fn y
|
||||
(n', delta) = run o n
|
||||
|
||||
de = delta * fn' y
|
||||
|
||||
biases' = biases - cmap (*alpha) de
|
||||
weights' = weights - cmap (*alpha) (input `outer` de)
|
||||
layer = Layer biases' weights' (fn, fn')
|
||||
|
||||
pass = weights #> de
|
||||
-- pass = weights #> de
|
||||
in (layer :- n', pass)-}
|
||||
|
||||
gd :: Session -> IO Session
|
||||
gd session = do
|
||||
seed <- newStdGen
|
||||
|
||||
let pairs = training session
|
||||
alpha = learningRate session
|
||||
net = network session
|
||||
|
||||
let n = length pairs
|
||||
|
||||
shuffled <- shuffleM pairs
|
||||
|
||||
let newnet = foldl' (\n (input, label) -> train input n label alpha) net pairs
|
||||
|
||||
return session { network = newnet
|
||||
, epoch = epoch session + 1
|
||||
}
|
||||
|
||||
sgd :: Session -> IO Session
|
||||
sgd session = do
|
||||
seed <- newStdGen
|
||||
|
||||
let pairs = training session
|
||||
bsize = batchSize session
|
||||
alpha = learningRate session
|
||||
net = network session
|
||||
|
||||
let n = length pairs
|
||||
iterations = n `div` bsize - 1
|
||||
|
||||
shuffled <- shuffleM pairs
|
||||
|
||||
let iter net i =
|
||||
let n = length pairs
|
||||
batch = take bsize . drop (i * bsize) $ shuffled
|
||||
batchInputs = map fst batch
|
||||
batchLabels = map snd batch
|
||||
batchPair = zip batchInputs batchLabels
|
||||
in foldl' (\n (input, label) -> train input n label alpha) net batchPair
|
||||
|
||||
let newnet = foldl' iter net [0..iterations]
|
||||
cost = crossEntropy (session { network = newnet })
|
||||
|
||||
let el = map (\(e, l, _) -> (e, l)) (chart session)
|
||||
ea = map (\(e, _, a) -> (e, a)) (chart session)
|
||||
|
||||
putStrLn $ (show $ epoch session) ++ " => " ++ (show cost) ++ " @ " ++ (show $ learningRate session)
|
||||
|
||||
toFile Chart.def "sgd.png" $ do
|
||||
Chart.layoutlr_title Chart..= "loss over time"
|
||||
Chart.plotLeft (Chart.line "loss" [el])
|
||||
Chart.plotRight (Chart.line "learningRate" [ea])
|
||||
|
||||
return session { network = newnet
|
||||
, epoch = epoch session + 1
|
||||
, chart = (epoch session, cost, learningRate session):chart session
|
||||
}
|
||||
|
||||
|
||||
accuracy :: Session -> Double
|
||||
accuracy session =
|
||||
let inputs = map fst (test session)
|
||||
labels = map snd (test session)
|
||||
|
||||
results = map (`forward` session) inputs
|
||||
rounded = map (map round . toList) results
|
||||
|
||||
equals = zipWith (==) rounded (map (map round . toList) labels)
|
||||
in genericLength (filter (== True) equals) / genericLength inputs
|
||||
|
||||
learningRateDecay :: (Double, Double) -> Session -> Session
|
||||
learningRateDecay (step, m) session =
|
||||
session { learningRate = max m $ learningRate session / step }
|
||||
|
||||
run :: (Session -> IO Session)
|
||||
-> Session -> IO Session
|
||||
run fn session = foldM (\s i -> fn s) session [0..epochs session]
|
||||
|
||||
factorial :: Int -> Int
|
||||
factorial 0 = 1
|
||||
factorial x = x * factorial (x - 1)
|
||||
|
||||
genSeed :: IO Seed
|
||||
genSeed = do
|
||||
@ -176,12 +339,7 @@ module Sibe
|
||||
replaceVector :: Vector Double -> Int -> Double -> Vector Double
|
||||
replaceVector vec index value =
|
||||
let list = toList vec
|
||||
in fromList $ rrow index list
|
||||
where
|
||||
rrow index [] = []
|
||||
rrow index (x:xs)
|
||||
| index == index = value:xs
|
||||
| otherwise = x : rrow (index + 1) xs
|
||||
in fromList $ take index list ++ value : drop (index + 1) list
|
||||
|
||||
clip :: Double -> (Double, Double) -> Double
|
||||
clip x (l, u) = min u (max l x)
|
||||
|
||||
0
src/Sibe/LogisticRegression.hs
Normal file
0
src/Sibe/LogisticRegression.hs
Normal file
129
src/Sibe/NLP.hs
Normal file
129
src/Sibe/NLP.hs
Normal file
@ -0,0 +1,129 @@
|
||||
module Sibe.NLP
|
||||
(Class,
|
||||
Document(..),
|
||||
ordNub,
|
||||
accuracy,
|
||||
recall,
|
||||
precision,
|
||||
fmeasure,
|
||||
cleanText,
|
||||
cleanDocuments,
|
||||
removeWords,
|
||||
removeStopwords,
|
||||
ngram,
|
||||
ngramText,
|
||||
)
|
||||
where
|
||||
import Data.List
|
||||
import Debug.Trace
|
||||
import qualified Data.Set as Set
|
||||
import Data.List.Split
|
||||
import Data.Maybe
|
||||
import Control.Arrow ((&&&))
|
||||
import Text.Regex.PCRE
|
||||
import Data.Char (isSpace, isNumber, toLower)
|
||||
import NLP.Stemmer
|
||||
|
||||
type Class = Int;
|
||||
|
||||
data Document = Document { text :: String
|
||||
, c :: Class
|
||||
} deriving (Eq, Show, Read)
|
||||
|
||||
|
||||
cleanText :: String -> String
|
||||
cleanText string =
|
||||
let puncs = filter (`notElem` ['!', '"', '#', '$', '%', '(', ')', '.', '?']) (trim string)
|
||||
spacify = foldl (\acc x -> replace x ' ' acc) puncs [',', '/', '-', '\n', '\r']
|
||||
stemmed = unwords $ map (stem Porter) (words spacify)
|
||||
nonumber = filter (not . isNumber) stemmed
|
||||
lower = map toLower nonumber
|
||||
in (unwords . words) lower -- remove unnecessary spaces
|
||||
where
|
||||
trim = f . f
|
||||
where
|
||||
f = reverse . dropWhile isSpace
|
||||
replace needle replacement =
|
||||
map (\c -> if c == needle then replacement else c)
|
||||
|
||||
cleanDocuments :: [Document] -> [Document]
|
||||
cleanDocuments documents =
|
||||
let cleaned = map (\(Document text c) -> Document (cleanText text) c) documents
|
||||
in cleaned
|
||||
|
||||
removeWords :: [String] -> [Document] -> [Document]
|
||||
removeWords ws documents =
|
||||
map (\(Document text c) -> Document (rm ws text) c) documents
|
||||
where
|
||||
rm list text =
|
||||
unwords $ filter (`notElem` list) (words text)
|
||||
|
||||
removeStopwords :: Int -> [Document] -> [Document]
|
||||
removeStopwords i documents =
|
||||
let wc = wordCounts (concatDocs documents)
|
||||
wlist = sortBy (\(_, a) (_, b) -> b `compare` a) wc
|
||||
stopwords = map fst (take i wlist)
|
||||
in removeWords stopwords documents
|
||||
where
|
||||
vocabulary x = ordNub (words x)
|
||||
countWordInDoc d w = genericLength (filter (==w) d)
|
||||
wordCounts x =
|
||||
let voc = vocabulary x
|
||||
in zip voc $ map (countWordInDoc (words x)) voc
|
||||
|
||||
concatDocs = concatMap (\(Document text _) -> text ++ " ")
|
||||
|
||||
ordNub :: (Ord a) => [a] -> [a]
|
||||
ordNub = go Set.empty
|
||||
where
|
||||
go _ [] = []
|
||||
go s (x:xs) = if x `Set.member` s then go s xs
|
||||
else x : go (Set.insert x s) xs
|
||||
|
||||
accuracy :: [(Int, (Int, Double))] -> Double
|
||||
accuracy results =
|
||||
let pairs = map (\(a, b) -> (a, fst b)) results
|
||||
correct = filter (uncurry (==)) pairs
|
||||
in genericLength correct / genericLength results
|
||||
|
||||
recall :: [(Int, (Int, Double))] -> Double
|
||||
recall results =
|
||||
let classes = ordNub (map fst results)
|
||||
s = sum (map rec classes) / genericLength classes
|
||||
in s
|
||||
where
|
||||
rec a =
|
||||
let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, (r, _)) -> c == a) results
|
||||
in t / y
|
||||
|
||||
precision :: [(Int, (Int, Double))] -> Double
|
||||
precision results =
|
||||
let classes = ordNub (map fst results)
|
||||
s = sum (map prec classes) / genericLength classes
|
||||
in s
|
||||
where
|
||||
prec a =
|
||||
let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, (r, _)) -> r == a) results
|
||||
in
|
||||
if y == 0
|
||||
then 0
|
||||
else t / y
|
||||
|
||||
fmeasure :: [(Int, (Int, Double))] -> Double
|
||||
fmeasure results =
|
||||
let r = recall results
|
||||
p = precision results
|
||||
in (2 * p * r) / (p + r)
|
||||
|
||||
ngram :: Int -> [Document] -> [Document]
|
||||
ngram n documents =
|
||||
map (\(Document text c) -> Document (ngramText n text) c) documents
|
||||
|
||||
ngramText :: Int -> String -> String
|
||||
ngramText n text =
|
||||
let ws = words text
|
||||
pairs = zip [0..] ws
|
||||
grams = map (\(i, w) -> concat . intersperse "_" $ w:((take (n - 1) . drop (i+1)) ws)) pairs
|
||||
in unwords ("<b>_":grams)
|
||||
@ -1,7 +1,7 @@
|
||||
module Sibe.NaiveBayes
|
||||
(Document(..),
|
||||
NB(..),
|
||||
train,
|
||||
initialize,
|
||||
run,
|
||||
session,
|
||||
ordNub,
|
||||
@ -19,21 +19,13 @@ module Sibe.NaiveBayes
|
||||
removeStopwords,
|
||||
)
|
||||
where
|
||||
import Sibe.NLP
|
||||
import Data.List
|
||||
import Debug.Trace
|
||||
import qualified Data.Set as Set
|
||||
import Data.List.Split
|
||||
import Data.Maybe
|
||||
import Control.Arrow ((&&&))
|
||||
import Text.Regex.PCRE
|
||||
import Data.Char (isSpace, isNumber, toLower)
|
||||
import NLP.Stemmer
|
||||
|
||||
type Class = Int;
|
||||
|
||||
data Document = Document { text :: String
|
||||
, c :: Class
|
||||
} deriving (Eq, Show, Read)
|
||||
|
||||
data NB = NB { documents :: [Document]
|
||||
, classes :: [(Class, Double)]
|
||||
@ -44,8 +36,8 @@ module Sibe.NaiveBayes
|
||||
, cgram :: [(Class, [(String, Int)])]
|
||||
} deriving (Eq, Show, Read)
|
||||
|
||||
train :: [Document] -> [Class] -> NB
|
||||
train documents classes =
|
||||
initialize :: [Document] -> [Class] -> NB
|
||||
initialize documents classes =
|
||||
let megadoc = concatDocs documents
|
||||
vocabulary = genericLength ((ordNub . words) megadoc)
|
||||
-- (class, prior probability)
|
||||
@ -83,17 +75,6 @@ module Sibe.NaiveBayes
|
||||
classWordsCounts x = wordsCount (classWords x) (classVocabulary x)
|
||||
classNGramCounts x = wordsCount (classNGramWords x) (ordNub $ classNGramWords x)
|
||||
|
||||
ngram :: Int -> [Document] -> [Document]
|
||||
ngram n documents =
|
||||
map (\(Document text c) -> Document (ngramText n text) c) documents
|
||||
|
||||
ngramText :: Int -> String -> String
|
||||
ngramText n text =
|
||||
let ws = words text
|
||||
pairs = zip [0..] ws
|
||||
grams = map (\(i, w) -> concat . intersperse "_" $ w:((take (n - 1) . drop (i+1)) ws)) pairs
|
||||
in unwords ("<b>_":grams)
|
||||
|
||||
session :: [Document] -> NB -> [(Class, (Class, Double))]
|
||||
session docs nb =
|
||||
let results = map (\(Document text c) -> (c, run text nb)) docs
|
||||
@ -143,91 +124,5 @@ module Sibe.NaiveBayes
|
||||
variance = sum (map ((^2) . subtract avg) x) / (genericLength x - 1)
|
||||
in sqrt variance
|
||||
|
||||
cleanText :: String -> String
|
||||
cleanText string =
|
||||
let puncs = filter (`notElem` ['!', '"', '#', '$', '%', '(', ')', '.', '?']) (trim string)
|
||||
spacify = foldl (\acc x -> replace x ' ' acc) puncs [',', '/', '-', '\n', '\r']
|
||||
stemmed = unwords $ map (stem Porter) (words spacify)
|
||||
nonumber = filter (not . isNumber) stemmed
|
||||
lower = map toLower nonumber
|
||||
in (unwords . words) lower -- remove unnecessary spaces
|
||||
where
|
||||
trim = f . f
|
||||
where
|
||||
f = reverse . dropWhile isSpace
|
||||
replace needle replacement =
|
||||
map (\c -> if c == needle then replacement else c)
|
||||
|
||||
cleanDocuments :: [Document] -> [Document]
|
||||
cleanDocuments documents =
|
||||
let cleaned = map (\(Document text c) -> Document (cleanText text) c) documents
|
||||
in cleaned
|
||||
|
||||
removeWords :: [String] -> [Document] -> [Document]
|
||||
removeWords ws documents =
|
||||
map (\(Document text c) -> Document (rm ws text) c) documents
|
||||
where
|
||||
rm list text =
|
||||
unwords $ filter (`notElem` list) (words text)
|
||||
|
||||
removeStopwords :: Int -> [Document] -> [Document]
|
||||
removeStopwords i documents =
|
||||
let wc = wordCounts (concatDocs documents)
|
||||
wlist = sortBy (\(_, a) (_, b) -> b `compare` a) wc
|
||||
stopwords = map fst (take i wlist)
|
||||
in removeWords stopwords documents
|
||||
where
|
||||
vocabulary x = ordNub (words x)
|
||||
countWordInDoc d w = genericLength (filter (==w) d)
|
||||
wordCounts x =
|
||||
let voc = vocabulary x
|
||||
in zip voc $ map (countWordInDoc (words x)) voc
|
||||
|
||||
concatDocs = concatMap (\(Document text _) -> text ++ " ")
|
||||
|
||||
l :: (Show a) => a -> a
|
||||
l a = trace (show a) a
|
||||
|
||||
ordNub :: (Ord a) => [a] -> [a]
|
||||
ordNub = go Set.empty
|
||||
where
|
||||
go _ [] = []
|
||||
go s (x:xs) = if x `Set.member` s then go s xs
|
||||
else x : go (Set.insert x s) xs
|
||||
|
||||
accuracy :: [(Int, (Int, Double))] -> Double
|
||||
accuracy results =
|
||||
let pairs = map (\(a, b) -> (a, fst b)) results
|
||||
correct = filter (uncurry (==)) pairs
|
||||
in genericLength correct / genericLength results
|
||||
|
||||
recall :: [(Int, (Int, Double))] -> Double
|
||||
recall results =
|
||||
let classes = ordNub (map fst results)
|
||||
s = sum (map rec classes) / genericLength classes
|
||||
in s
|
||||
where
|
||||
rec a =
|
||||
let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, (r, _)) -> c == a) results
|
||||
in t / y
|
||||
|
||||
precision :: [(Int, (Int, Double))] -> Double
|
||||
precision results =
|
||||
let classes = ordNub (map fst results)
|
||||
s = sum (map prec classes) / genericLength classes
|
||||
in s
|
||||
where
|
||||
prec a =
|
||||
let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, (r, _)) -> r == a) results
|
||||
in
|
||||
if y == 0
|
||||
then 0
|
||||
else t / y
|
||||
|
||||
fmeasure :: [(Int, (Int, Double))] -> Double
|
||||
fmeasure results =
|
||||
let r = recall results
|
||||
p = precision results
|
||||
in (2 * p * r) / (p + r)
|
||||
|
||||
74
stack.yaml
74
stack.yaml
@ -1,40 +1,5 @@
|
||||
# This file was automatically generated by 'stack init'
|
||||
#
|
||||
# Some commonly used options have been documented as comments in this file.
|
||||
# For advanced use and comprehensive documentation of the format, please see:
|
||||
# http://docs.haskellstack.org/en/stable/yaml_configuration/
|
||||
|
||||
# Resolver to choose a 'specific' stackage snapshot or a compiler version.
|
||||
# A snapshot resolver dictates the compiler version and the set of packages
|
||||
# to be used for project dependencies. For example:
|
||||
#
|
||||
# resolver: lts-3.5
|
||||
# resolver: nightly-2015-09-21
|
||||
# resolver: ghc-7.10.2
|
||||
# resolver: ghcjs-0.1.0_ghc-7.10.2
|
||||
# resolver:
|
||||
# name: custom-snapshot
|
||||
# location: "./custom-snapshot.yaml"
|
||||
resolver: lts-6.7
|
||||
|
||||
# User packages to be built.
|
||||
# Various formats can be used as shown in the example below.
|
||||
#
|
||||
# packages:
|
||||
# - some-directory
|
||||
# - https://example.com/foo/bar/baz-0.0.2.tar.gz
|
||||
# - location:
|
||||
# git: https://github.com/commercialhaskell/stack.git
|
||||
# commit: e7b331f14bcffb8367cd58fbfc8b40ec7642100a
|
||||
# - location: https://github.com/commercialhaskell/stack/commit/e7b331f14bcffb8367cd58fbfc8b40ec7642100a
|
||||
# extra-dep: true
|
||||
# subdirs:
|
||||
# - auto-update
|
||||
# - wai
|
||||
#
|
||||
# A package marked 'extra-dep: true' will only be built if demanded by a
|
||||
# non-dependency (i.e. a user package), and its test suites and benchmarks
|
||||
# will not be run. This is useful for tweaking upstream packages.
|
||||
packages:
|
||||
- location:
|
||||
git: git@github.com:albertoruiz/hmatrix.git
|
||||
@ -42,36 +7,11 @@ packages:
|
||||
subdirs:
|
||||
- packages/base
|
||||
- .
|
||||
- http://hackage.haskell.org/package/containers-0.5.7.1/containers-0.5.7.1.tar.gz
|
||||
- http://hackage.haskell.org/package/text-1.2.2.1/text-1.2.2.1.tar.gz
|
||||
- http://hackage.haskell.org/package/stemmer-0.5.2/stemmer-0.5.2.tar.gz
|
||||
|
||||
# Dependency packages to be pulled from upstream that are not in the resolver
|
||||
# (e.g., acme-missiles-0.3)
|
||||
extra-deps: []
|
||||
|
||||
# Override default flag values for local packages and extra-deps
|
||||
flags: {}
|
||||
|
||||
# Extra package databases containing global packages
|
||||
extra-package-dbs: []
|
||||
|
||||
# Control whether we use the GHC we find on the path
|
||||
# system-ghc: true
|
||||
#
|
||||
# Require a specific version of stack, using version ranges
|
||||
# require-stack-version: -any # Default
|
||||
# require-stack-version: ">=1.1"
|
||||
#
|
||||
# Override the architecture used by stack, especially useful on Windows
|
||||
# arch: i386
|
||||
# arch: x86_64
|
||||
#
|
||||
# Extra directories used by stack for building
|
||||
# extra-include-dirs: [/path/to/dir]
|
||||
# extra-lib-dirs: [/path/to/dir]
|
||||
#
|
||||
# Allow a newer minor version of GHC than the snapshot specifies
|
||||
# compiler-check: newer-minor
|
||||
|
||||
system-ghc: false
|
||||
extra-deps:
|
||||
- directory-1.2.7.0
|
||||
- text-1.2.2.1
|
||||
- stemmer-0.5.2
|
||||
- containers-0.5.7.1
|
||||
- Chart-1.8
|
||||
- Chart-cairo-1.8
|
||||
|
||||
Loading…
Reference in New Issue
Block a user