AI.Network

Efficient representation of an Artificial Neural Network using vector to represent the weights between each layer

This module provides the neural network data representation that will be used extensively

Documentation

data Network

The representation of an Artificial Neural Network

Constructors

Network

Fields

activation :: Double -> Double

The activation function for each neuron

derivative :: Double -> Double

The derivative of the activation function

lambda :: Double

The regularization constant

weights :: Vector Double

The vector of the weights between each layer of the neural network

architecture :: [Int]

The architecture of the neural networks.

e.g., a network of an architecture of 2-3-1 would have an architecture representation of [2,3,1]

NOTE: The library will automatically create a bias neuron in each layer, so you do not need to state them explicitly

toActivation :: Network -> Double -> Double

toDerivative :: Network -> Double -> Double

toLambda :: Network -> Double

toWeights :: Network -> Vector Double

toWeightMatrices :: Network -> [Matrix Double]

Get the list of matrices of weights between each layer. This can be more useful than the barebone vector representation of the weights

toArchitecture :: Network -> [Int]

setActivation :: Network -> (Double -> Double) -> Network

setDerivative :: Network -> (Double -> Double) -> Network

setLambda :: Network -> Double -> Network

setWeights :: Network -> Vector Double -> Network

setArchitecture :: Network -> [Int] -> Network