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Linux:

$ git clone https://github.com/YuriyLisovskiy/NeuralNetwork.git
$ cd NeuralNetwork/
$ virtualenv venv
$ source venv/bin/activate
$ pip install -r requirements.txt

Windows:

$ git clone https://github.com/YuriyLisovskiy/NeuralNetwork.git
$ cd NeuralNetwork/
$ virtualenv venv
$ venv/Scripts/activate
$ pip install -r requirements.txt

Run demo:

$ python runner.py test

• From neural_network package import network:

   from neural_network.network.net import NeuralNetwork

• Create training data for training neural network, example:

   training_data = [
       ([0, 0, 0], 0),
       ([0, 0, 1], 1),
       ([0, 1, 0], 0),
       ([0, 1, 1], 0),
       ([1, 0, 0], 1),
       ([1, 0, 1], 1),
       ([1, 1, 0], 0),
       ([1, 1, 1], 1)
   ]

• Create new neural network using config/config.py or custom parameters, example:

   INPUT_LAYER = [3]
   HIDDEN_LAYERS = [5, 4, 2]
   OUTPUT_LAYER = [1]
   ITERATIONS = 10000
   LEARNING_RATE = 0.007

   new_net = NeuralNetwork(
       input_layer=INPUT_LAYER,
       hidden_layers=HIDDEN_LAYERS,
       output_layer=OUTPUT_LAYER,
       learning_rate=LEARNING_RATE,
       log=False
   )

• Train the network:

   new_net.train(
       data=training_data,
       iterations=ITERATIONS,
       log=False
   )

• Now network is ready to work, example:

   def get_prediction(input_data):
       result = new_net.predict(input_data)
       return result >= 0.5

   if __name__ == '__main__':
       print(get_prediction([0, 1, 0]))

• Yuriy Lisovskiy

This project is licensed under the BSD-2-Clause License - see the LICENSE file for details.