sibe/README.md

sibe

A simple Machine Learning library.

notMNIST dataset, cross-entropy loss, learning rate decay and sgd:

Simple neural network

    let a = (sigmoid, sigmoid') -- activation function
        -- random network, seed 0, values between -1 and 1,
        -- two inputs, two nodes in hidden layer and a single output
        rnetwork = randomNetwork 0 (-1, 1) 2 [(2, a)] (1, a)

        -- inputs and labels
        inputs = [vector [0, 1], vector [1, 0], vector [1, 1], vector [0, 0]]
        labels = [vector [1], vector [1], vector [0], vector [0]]

        -- define the session which includes parameters
        session = def { network = rnetwork
                      , learningRate = 0.5
                      , epochs = 1000
                      , training = zip inputs labels
                      , test = zip inputs labels
                      } :: Session

        initialCost = crossEntropy session

    -- run gradient descent
    -- you can also use `sgd`, see the notmnist example
    newsession <- run gd session

    let results = map (`forward` newsession) inputs
        rounded = map (map round . toList) results

        cost = crossEntropy newsession
    
    putStrLn $ "- initial cost (cross-entropy): " ++ show initialCost
    putStrLn $ "- actual result: " ++ show results
    putStrLn $ "- rounded result: " ++ show rounded
    putStrLn $ "- cost (cross-entropy): " ++ show cost

Examples

# neural network examples
stack exec example-xor
stack exec example-424
# notMNIST dataset, achieves ~87% accuracy using exponential learning rate decay
stack exec example-notmnist

# Naive Bayes document classifier, using Reuters dataset
# using Porter stemming, stopword elimination and a few custom techniques.
# The dataset is imbalanced which causes the classifier to be biased towards some classes (earn, acq, ...)
# to workaround the imbalanced dataset problem, there is a --top-ten option which classifies only top 10 popular
# classes, with evenly split datasets (100 for each), this increases F Measure significantly, along with ~10% of improved accuracy
# N-Grams don't seem to help us much here (or maybe my implementation is wrong!), using bigrams increases
# accuracy, while decreasing F-Measure slightly.
stack exec example-naivebayes-doc-classifier -- --verbose
stack exec example-naivebayes-doc-classifier -- --verbose --top-ten