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| Description |
| Layer module, defining functions to work on a neural network layer, which is a list of neurons
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| Synopsis |
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| Layer creation
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| createSigmoidLayerU :: Int -> Double -> UArr Double -> [Neuron] |
| Creates a layer compound of n neurons with the Sigmoid transfer function, all having the given threshold and weights.
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| createHeavysideLayerU :: Int -> Double -> UArr Double -> [Neuron] |
| Creates a layer compound of n neurons with the Heavyside transfer function, all having the given threshold and weights.
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| createSigmoidLayer :: Int -> Double -> [Double] -> [Neuron] |
| Creates a layer compound of n neurons with the sigmoid transfer function, all having the given threshold and weights.
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| createHeavysideLayer :: Int -> Double -> [Double] -> [Neuron] |
| Creates a layer compound of n neurons with the sigmoid transfer function, all having the given threshold and weights.
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| Computation
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| computeLayerU :: [Neuron] -> UArr Double -> UArr Double |
| Computes the outputs of each Neuron of the layer
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| computeLayer :: [Neuron] -> [Double] -> [Double] |
| Computes the outputs of each Neuron of the layer
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| Learning
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| learnSampleLayerU :: Double -> [Neuron] -> (UArr Double, UArr Double) -> [Neuron] |
| Trains each neuron with the given sample and the given learning ratio
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| learnSampleLayer :: Double -> [Neuron] -> ([Double], [Double]) -> [Neuron] |
| Trains each neuron with the given sample and the given learning ratio
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| learnSamplesLayerU :: Double -> [Neuron] -> [(UArr Double, UArr Double)] -> [Neuron] |
| Trains each neuron with the given samples and the given learning ratio
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| learnSamplesLayer :: Double -> [Neuron] -> [([Double], [Double])] -> [Neuron] |
| Trains each neuron with the given samples and the given learning ratio
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| Quadratic Error
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| quadErrorU :: [Neuron] -> (UArr Double, UArr Double) -> Double |
| Returns the quadratic error of a layer for a given sample
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| quadError :: [Neuron] -> ([Double], [Double]) -> Double |
| Returns the quadratic error of a layer for a given sample
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| Produced by Haddock version 2.4.2 |
