34 lines
1.3 KiB
Haskell
34 lines
1.3 KiB
Haskell
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 learning_rate = 0.5
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(iterations, epochs) = (2, 1000)
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a = (logistic, logistic')
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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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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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network = session inputs rnetwork labels learning_rate (iterations, epochs)
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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 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 "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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