A L-Layers XOR Neural Network using only Python and Numpy that learns to predict the XOR logic gates.
The script was initially made as a .ipynb jupyter file and later refactored as a class and a script where arguments can be passed to the neural network.
There are two files:
nn.py: the main script that uses our NeuralNetwork class stored in the other file ;nn_xor_class.py: our NeuralNetwork class.
You can change the behavior of the Neural Network by using arguments when running the script. For example, you can change the activation function of the hidden layers, the learning rate etc. Here are the arguments allowed when running nn.py. All arguments are optional.
python nn.py [-h] [-l LAYERS] [-u UNITS] [-s SIZE] [-i ITERATIONS] [-r LEARNING_RATE]
[-a {sigmoid,tanH,relu,leakyRelu}]
-h, --help show this help message and exit
-l LAYERS, --layers LAYERS
Number of layers in your NN (including the output layer). Default: 4.
-u UNITS, --units UNITS
Number of units in each hidden layer separated by a
comma (including output layer). Default:
4,2,1.
-s SIZE, --size SIZE How many examples should be generated in our training
set. Default: 1000.
-i ITERATIONS, --iterations ITERATIONS
Choose the number of iterations we want. Default: 10000.
-r LEARNING_RATE, --learning-rate LEARNING_RATE
Pick a Learning rate for your neural Network. Default: 1.5.
-a {sigmoid,tanH,relu,leakyRelu}, --activation {sigmoid,tanH,relu,leakyRelu}
Activation function for your hidden layers. The output
layer will always be a sigmoid. Default: "tanH".