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| Description |
| Net module, defining functions to work on a neural network, which is a list of list of neurons
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| Synopsis |
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| Documentation |
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| check :: [[Neuron]] -> Bool |
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| nn :: [[Neuron]] -> [[Neuron]] |
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| Computation
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| computeNetU :: [[Neuron]] -> UArr Double -> UArr Double |
| Computes the output of the given neural net on the given inputs
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| computeNet :: [[Neuron]] -> [Double] -> [Double] |
| Computes the output of the given neural net on the given inputs
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| Quadratic Error
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| quadErrorNetU :: [[Neuron]] -> (UArr Double, UArr Double) -> Double |
| Returns the quadratic error of the neural network on the given sample
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| quadErrorNet :: [[Neuron]] -> ([Double], [Double]) -> Double |
| Returns the quadratic error of the neural network on the given sample
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| globalQuadErrorNetU :: [[Neuron]] -> [(UArr Double, UArr Double)] -> Double |
| Returns the quadratic error of the neural network on the given samples
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| globalQuadErrorNet :: [[Neuron]] -> [([Double], [Double])] -> Double |
| Returns the quadratic error of the neural network on the given samples
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| Learning
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| backPropU :: Double -> [[Neuron]] -> (UArr Double, UArr Double) -> [[Neuron]] |
| Train the given neural network using the backpropagation algorithm on the given sample with the given learning ratio (alpha)
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| backProp :: Double -> [[Neuron]] -> ([Double], [Double]) -> [[Neuron]] |
| Train the given neural network using the backpropagation algorithm on the given sample with the given learning ratio (alpha)
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| trainAux :: Double -> [[Neuron]] -> [(UArr Double, UArr Double)] -> [[Neuron]] |
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| trainU :: Double -> Double -> [[Neuron]] -> [(UArr Double, UArr Double)] -> [[Neuron]] |
| Train the given neural network on the given samples using the backpropagation algorithm using the given learning ratio (alpha) and the given desired maximal bound for the global quadratic error on the samples (epsilon)
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| train :: Double -> Double -> [[Neuron]] -> [([Double], [Double])] -> [[Neuron]] |
| Train the given neural network on the given samples using the backpropagation algorithm using the given learning ratio (alpha) and the given desired maximal bound for the global quadratic error on the samples (epsilon)
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| Produced by Haddock version 2.4.2 |
