| Safe Haskell | None |
|---|---|
| Language | Haskell98 |
Network.Trainer
- data BackpropTrainer a = BackpropTrainer {
- eta :: a
- cost :: CostFunction a
- cost' :: CostFunction' a
- type CostFunction a = Vector a -> Vector a -> a
- type CostFunction' a = Vector a -> Vector a -> Vector a
- type TrainingData a = (Vector a, Vector a)
- type Selection a = [TrainingData a] -> [[TrainingData a]]
- type TrainCompletionPredicate a = Network a -> BackpropTrainer a -> [TrainingData a] -> Int -> Bool
- trainNTimes :: (Floating (Vector a), Container Vector a, Product a) => Network a -> BackpropTrainer a -> Selection a -> [TrainingData a] -> Int -> Network a
- trainUntilErrorLessThan :: (Floating (Vector a), Container Vector a, Product a, Ord a) => Network a -> BackpropTrainer a -> Selection a -> [TrainingData a] -> a -> Network a
- trainUntil :: (Floating (Vector a), Container Vector a, Product a) => Network a -> BackpropTrainer a -> Selection a -> [TrainingData a] -> TrainCompletionPredicate a -> Int -> Network a
- quadraticCost :: (Floating (Vector a), Container Vector a) => Vector a -> Vector a -> a
- quadraticCost' :: Floating (Vector a) => Vector a -> Vector a -> Vector a
- minibatch :: (Floating (Vector a), Container Vector a) => Int -> [TrainingData a] -> [[TrainingData a]]
- online :: (Floating (Vector a), Container Vector a) => [TrainingData a] -> [[TrainingData a]]
- backprop :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> [TrainingData a] -> Network a
- inputs :: (Floating (Vector a), Container Vector a, Product a) => Vector a -> Network a -> [Vector a]
- outputs :: (Floating (Vector a), Container Vector a, Product a) => Vector a -> Network a -> [Vector a]
- deltas :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> TrainingData a -> [Vector a]
- hiddenDeltas :: (Floating (Vector a), Container Vector a, Product a) => Network a -> Vector a -> [Vector a] -> [Vector a]
- calculateNablas :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> Network a -> TrainingData a -> Network a
- fit :: (Floating (Vector a), Container Vector a, Product a) => Selection a -> BackpropTrainer a -> Network a -> [TrainingData a] -> Network a
- evaluate :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> TrainingData a -> a
Documentation
data BackpropTrainer a Source
Trainer is a typeclass for all trainer types - a trainer will take in an instance of itself, a network, a list of training data, and return a new network trained on the data. class Trainer a where fit :: (Floating b) => a -> Network b -> [TrainingData b] -> Network b
A BackpropTrainer performs simple backpropagation on a neural network. It can be used as the basis for more complex trainers.
Constructors
| BackpropTrainer | |
Fields
| |
type CostFunction a = Vector a -> Vector a -> a Source
A CostFunction is used for evaluating a network's performance on a given input
type CostFunction' a = Vector a -> Vector a -> Vector a Source
A CostFunction' (derivative) is used in backPropagation
type TrainingData a = (Vector a, Vector a) Source
A tuple of (input, expected output)
type Selection a = [TrainingData a] -> [[TrainingData a]] Source
A selection function for performing gradient descent
type TrainCompletionPredicate a = Network a -> BackpropTrainer a -> [TrainingData a] -> Int -> Bool Source
A predicate (given a network, trainer, a list of training data, and the number of [fit]s performed) that tells the trainer to stop training
trainNTimes :: (Floating (Vector a), Container Vector a, Product a) => Network a -> BackpropTrainer a -> Selection a -> [TrainingData a] -> Int -> Network a Source
Given a network, a trainer, a list of training data, and N, this function trains the network with the list of training data N times
trainUntilErrorLessThan :: (Floating (Vector a), Container Vector a, Product a, Ord a) => Network a -> BackpropTrainer a -> Selection a -> [TrainingData a] -> a -> Network a Source
Given a network, a trainer, a list of training data, and an error value, this function trains the network with the list of training data until the error of the network (calculated by averaging the errors of each training data) is less than the given error value
trainUntil :: (Floating (Vector a), Container Vector a, Product a) => Network a -> BackpropTrainer a -> Selection a -> [TrainingData a] -> TrainCompletionPredicate a -> Int -> Network a Source
This function trains a network until a given TrainCompletionPredicate is satisfied.
quadraticCost :: (Floating (Vector a), Container Vector a) => Vector a -> Vector a -> a Source
The quadratic cost function (1/2) * sum (y - a) ^ 2
quadraticCost' :: Floating (Vector a) => Vector a -> Vector a -> Vector a Source
The derivative of the quadratic cost function sum (y - a)
minibatch :: (Floating (Vector a), Container Vector a) => Int -> [TrainingData a] -> [[TrainingData a]] Source
The minibatch function becomes a Selection when partially applied with the minibatch size
online :: (Floating (Vector a), Container Vector a) => [TrainingData a] -> [[TrainingData a]] Source
If we want to train the network online
backprop :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> [TrainingData a] -> Network a Source
Perform backpropagation on a single training data instance.
inputs :: (Floating (Vector a), Container Vector a, Product a) => Vector a -> Network a -> [Vector a] Source
The inputs function performs a similar task to outputs, but returns a list of vectors of unactivated inputs
outputs :: (Floating (Vector a), Container Vector a, Product a) => Vector a -> Network a -> [Vector a] Source
The outputs function scans over each layer of the network and stores the activated results
deltas :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> TrainingData a -> [Vector a] Source
The deltas function returns a list of layer deltas.
hiddenDeltas :: (Floating (Vector a), Container Vector a, Product a) => Network a -> Vector a -> [Vector a] -> [Vector a] Source
Compute the hidden layer deltas
calculateNablas :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> Network a -> TrainingData a -> Network a Source
Calculate the nablas for a minibatch and return them as a network (so each weight and bias gets its own nabla).
fit :: (Floating (Vector a), Container Vector a, Product a) => Selection a -> BackpropTrainer a -> Network a -> [TrainingData a] -> Network a Source
Declare the BackpropTrainer to be an instance of Trainer. instance (Floating a) => Trainer (BackpropTrainer a) where
evaluate :: (Floating (Vector a), Container Vector a, Product a) => BackpropTrainer a -> Network a -> TrainingData a -> a Source
Use the cost function to determine the error of a network