41 lines
1.4 KiB
Haskell
41 lines
1.4 KiB
Haskell
module Main where
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import Numeric.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 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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session = def { network = rnetwork
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, learningRate = 0.8
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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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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 (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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putStrLn $ "- cost (cross-entropy): " ++ show cost
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