36 lines
1.2 KiB
Haskell
36 lines
1.2 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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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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