sibe ==== A simple Machine Learning library. ## Simple neural network ```haskell 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 ```bash # neural network examples stack exec example-xor stack exec example-424 # notMNIST dataset, achieves ~87.5% accuracy after 9 epochs (2 minutes) 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 ``` ### notMNIST notMNIST dataset, sigmoid hidden layer, cross-entropy loss, learning rate decay and sgd ([`notmnist.hs`](https://github.com/mdibaiee/sibe/blob/master/examples/notmnist.hs)): ![notMNIST](https://github.com/mdibaiee/sibe/blob/master/notmnist.png?raw=true) notMNIST dataset, relu hidden layer, cross-entropy loss, learning rate decay and sgd ([`notmnist.hs`](https://github.com/mdibaiee/sibe/blob/master/examples/notmnist.hs)): ![notMNIST](https://github.com/mdibaiee/sibe/blob/master/notmnist.png?raw=true)