# onn

**Repository Path**: frontxiang/onn

## Basic Information

- **Project Name**: onn
- **Description**: Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-06-07
- **Last Updated**: 2022-04-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Online Neural Network (ONN)

This is a Pytorch implementation of the [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://arxiv.org/abs/1711.03705) 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
```python
#Importing Library
import numpy as np
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.)

# Non-linear Contextual Bandit Algorithm (ONN_THS)

The ONN_THS acts like a non-linear contextual bandit (a reinforcement learning algorithm). This algorithm works with the non-linear exploitation factor (ONN) plus an exploration factor provided by Thompson Sampling algorithm. The ONN_THS works with 'select' and 'reward' actions. For more detailed examples, please look at the jupyter notebook file in this repository. This algorithm was created by me to solve a problem in the company I work.

The great thing about this algorithm is that it can be used in an online manner and it has a non-linear exploitation. This algorithm can learn different kind of data in a reinforcement learning way.

## How to use
```python
#Importing Library
import numpy as np
from onn.OnlineNeuralNetwork import ONN_THS

#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_THS(features_size=2, max_num_hidden_layers=5, qtd_neuron_per_hidden_layer=10, n_classes=2)

#Select an action
arm_selected, exploration_factor = onn_network.predict(np.asarray([[0.1, 0.2]]))

#Reward an action
onn_network.partial_fit(np.asarray([[0.1, 0.2]]), np.asarray([arm_selected]), exploration_factor)

```

## Contributors
- [Alison de Andrade Carrera](https://github.com/alison-carrera)
- [Fábio Silva Vilas Boas](https://github.com/fabiosvb)
- [Daniel Araújo](https://github.com/danielcoaraujo)

## References
- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://arxiv.org/abs/1711.03705)
- [A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit](https://arxiv.org/pdf/1510.00757.pdf)