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        "description": "# Online Neural Network (ONN)\n\nThis 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.\n\n## Installing\n```\npip install onn\n```\n\n## How to use\n```python\n#Importing Library\nimport numpy as np\nfrom onn.OnlineNeuralNetwork import ONN\n\n#Starting a neural network with feature size of 2, hidden layers expansible until 5, number of neuron per hidden layer = 10 #and two classes.\nonn_network = ONN(features_size=2, max_num_hidden_layers=5, qtd_neuron_per_hidden_layer=10, n_classes=2)\n\n#Do a partial training\nonn_network.partial_fit(np.asarray([[0.1, 0.2]]), np.asarray([0]))\nonn_network.partial_fit(np.asarray([[0.8, 0.5]]), np.asarray([1]))\n\n#Predict classes\npredictions = onn_network.predict(np.asarray([[0.1, 0.2], [0.8, 0.5]]))\n\nPredictions -- array([1, 0])\n\n```\n\n## New features\n\n- The algortihm works with batch now. (It is not recommended because this is an online approach. It is useful for experimentation.)\n- The algorithm can use CUDA if available. (If the network is very small, it is not recommended. The CPU will process more fast.)\n\n# Non-linear Contextual Bandit Algorithm (ONN_THS)\n\nThe 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.\n\nThe 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.\n\n## How to use\n```python\n#Importing Library\nimport numpy as np\nfrom onn.OnlineNeuralNetwork import ONN_THS\n\n#Starting a neural network with feature size of 2, hidden layers expansible until 5, number of neuron per hidden layer = 10 #and two classes.\nonn_network = ONN_THS(features_size=2, max_num_hidden_layers=5, qtd_neuron_per_hidden_layer=10, n_classes=2)\n\n#Select an action\narm_selected, exploration_factor = onn_network.predict(np.asarray([[0.1, 0.2]]))\n\n#Reward an action\nonn_network.partial_fit(np.asarray([[0.1, 0.2]]), np.asarray([arm_selected]), exploration_factor)\n\n```\n\n## Contributors\n- [Alison de Andrade Carrera](https://github.com/alison-carrera)\n- [F\u00e1bio Silva Vilas Boas](https://github.com/fabiosvb)\n- [Daniel Ara\u00fajo](https://github.com/danielcoaraujo)\n\n## References\n- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://arxiv.org/abs/1711.03705)\n- [A Survey of Online Experiment Design with the Stochastic Multi-Armed Bandit](https://arxiv.org/pdf/1510.00757.pdf)",
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