Implementing the backpropagation algorithm for Neural Networks
This python program implements the backpropagation algorithm for Neural Networks. There are two steps:
- Pre-processing the dataset. The two arguments for the program:
- input path of the raw dataset
- output path of the pre-processed dataset
- Training a Neural Network - Uses the processed dataset to build a neural network. The input parameters to the neural net are:
- input dataset – complete path of the post-processed input dataset
- training percent – percentage of the dataset to be used for training
- maximum_iterations – Maximum number of iterations that the algorithm will run. This parameter is used so that the program terminates in a reasonable time.
- number of hidden layers
- number of neurons in each hidden layer
Pandas is used for reading/pre-processing data.