onn 0.1.2

Online Neural Network

Online Neural Network (ONN)

This is a Pytorch implementation of the Online Deep Learning: Learning Deep Neural Networks on the Fly paper. This algorithm contains a new backpropagation approach called Hedge Backpropagation and it is useful for online learning. In this algorithm you model a overnetwork architeture and the algorithm will try to turn on or turn off some of the hidden layers automatically. This algorithm uses the first hidden layer to train/predict but if it is going bad it starts to use another layers automatically. For more informations read the paper in the 'References' section.

Installing

pip install onn

How to use

#Importing Library
from onn.OnlineNeuralNetwork import ONN

#Starting a neural network with feature size of 2, hidden layers expansible until 5, number of neuron per hidden layer = 10 #and two classes.
onn_network = ONN(features_size=2, max_num_hidden_layers=5, qtd_neuron_per_hidden_layer=10, n_classes=2)

#Do a partial training
onn_network.partial_fit(np.asarray([[0.1, 0.2]]), np.asarray([0]))
onn_network.partial_fit(np.asarray([[0.8, 0.5]]), np.asarray([1]))

#Predict classes
predictions = onn_network.predict(np.asarray([[0.1, 0.2], [0.8, 0.5]]))

Predictions -- array([1, 0])

New features

• The algortihm works with batch now. (It is not recommended because this is an online approach. It is useful for experimentation.)
• The algorithm can use CUDA if available. (If the network is very small, it is not recommended. The CPU will process more fast.)

Contributors

• Alison de Andrade Carrera

References

• Online Deep Learning: Learning Deep Neural Networks on the Fly