A custom neural net for recoginzing handwritten digits, with a serious nod to I Am Trask. The neural net is pre-trained on the MNIST dataset (run on an AWS instance). And the user input is rescaled and tested.

sketch.js is a library created by Michael Bleigh to allow the user to draw the image of a digit. It has been modified to pull a drawn image from the page before being resized and sent to the neural network for classifaciton.

• Analize Results
• Analize correlation of misclassification and actual numbers (7's tend to be classified as 2, etc.)
• Train net on rotated images for more robust recognition
• Visualizations of each layer of the net (in d3 or bokeh)
• Provide a selection of neural net architectures to allow a user to swap out and see the different vizualations and results. (Results data will be stored by specific architecture.)

Navigate to: Finnegan

The baseline architecture for the net is on Github, here, and the code for the webapp (including Finnegan) be found here.

Full documentation hosted here

To adjust hyper-parameters or choose dataset: Open net_launch.py and adjust as needed at the bottom of the file. The dataset can be switched by commenting out the appropriate line at the bottom and uncommneting the other. Or simply:

from Network import network

And feed it the appropriate parameters.