A python implementation of a Fully Connected Neural Network from scratch using numpy.
run.py- Sample usage for the Neural Network on a noisy sine wave.nn.py- Implementation of Fully Connected layer and Neural Network (NN) class
Running run.py will start a training on the noisy sine function.
While training, metrics will print every 10th epoch. Sample output:
starting training...
epoch: 10, train RMSE: 3.052E-01, val RMSE: 2.993E-01
epoch: 20, train RMSE: 2.998E-01, val RMSE: 2.970E-01
epoch: 30, train RMSE: 3.000E-01, val RMSE: 2.941E-01
...
Two plots will be shown after the training finishes. An error vs. epochs plot and a predictions vs. ground truth plot. Samples are included in images/.
The implemented algorithm is based on the book Tom M. Mitchell - Machine Learning. It explains neural networks and the backpropagation algorithm.
This code was developed on another repository and was then ported to this one.