| Maintainer | Kiet Lam <ktklam9@gmail.com> |
|---|
AI.Signatures
Description
This module provides the signatures for needed functions in a neural network
- type ActivationFunction = Double -> Double
- type DerivativeFunction = Double -> Double
- type ErrorFunction = Vector Double -> Vector Double -> Double
- type CostFunction = Network -> Matrix Double -> Matrix Double -> Double
- type CostDerivative = Network -> Matrix Double -> Matrix Double -> Matrix Double -> Matrix Double
- type GradientFunction = CostFunction -> CostDerivative -> Network -> Matrix Double -> Matrix Double -> Vector Double
Documentation
type ActivationFunction = Double -> DoubleSource
Type that represents the activation function
type DerivativeFunction = Double -> DoubleSource
Type that represents the derivative of the activation function
NOTE: The derivative can be non-trivial and must be continuous
type ErrorFunctionSource
Arguments
| = Vector Double | The calculated output vector |
| -> Vector Double | The expected output vector |
| -> Double | Returns the error of how far off the calculated vector is from the expected vector |
Type that represents the error function between the calculated output vector and the expected output vector
type CostFunctionSource
Arguments
| = Network | The neural networks of interest |
| -> Matrix Double | The input matrix, where the ith row is the input vector of a training set |
| -> Matrix Double | The expected output matrix, where the ith row is the expected output vector of a training set |
| -> Double | Returns the cost of the calculated output vector from the neural network and the given expected output vector |
Type that represents the function that can calculate the total cost of the neural networks given the neural networks, the input matrix and an expected output matrix
type CostDerivativeSource
Arguments
| = Network | The neural networks of interest |
| -> Matrix Double | The matrix of inputs where the ith row is the ith training set |
| -> Matrix Double | The matrix of calculated outputs where the ith row is the ith training set |
| -> Matrix Double | The matrix of expected outputs where the ith row is the ith expected output of of the training set |
| -> Matrix Double | Returns the matrix of the derivatives of the cost of the output nodes compared to the expected matrix |
Type that represents the cost function derivative. on the output nodes
Arguments
| = CostFunction | The cost function |
| -> CostDerivative | The cost derivative |
| -> Network | The neural network |
| -> Matrix Double | The input matrix |
| -> Matrix Double | The expected output matrix |
| -> Vector Double | Returns the gradient vector of the weights |
The type to represent a function that can calculate the gradient vector of the weights of the neural network
NOTE: Must be supplied a function to calculate the cost, the cost derivative of the output neurons, the neural network the input matrix, and the expected output matrix