50 lines
1.5 KiB
Haskell
50 lines
1.5 KiB
Haskell
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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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