Generic Neural Network Python Library

• only supports sigmoid activation
• n fully connected sequential layers (dense)
• MSE loss
• stochastic gradient descent

NOTE: the code for this library has been greately enhanced for a subsequent project link here

This program creates a neural network programmatically with the following parameters:

• number of inputs
• number of neurons in hidden layer 1, ..., number of neurons in hidden layer n
• number of outputs
• learning rate

Once created the Neural Network has two functions:

• Forward Propagation: to generate a prediction or guess based on the inputs
• Train: to modify the inner weights and biases based on given inputs and target outputs

For testing purposes the XOR algorithm is implemented in the main.py script.

TO DO:

• multiple activation functions (ReLu, linear, Tanh, etc.)
• multiple optimizers (Adam, RMSProp, SGD Momentum, etc.)
• batch and epoch training schedules
• save and load trained model to file