This is a project to demonstrate how tuning different hyperparameters of a neural-network lead to a change in accuracy and time-taken. This uses the widgets library of IPython for Jupyter notebooks, and uses Voila to synthesize it into a static web-site, free of code.
The neural-net example here takes the vectorized MNIST dataset as input and passes it through 3 hidden layers to predict the input image class (i.e., value from 0-9) using a cross-entropy loss function.
Using Voíla, we synthesize the notebook into a static web-page with active widgets, and thus get a web-page where we can play around with the inputs and get the desired output.
To get this project working as intented, do the following:
- Install torch - https://github.com/pytorch/pytorch
- Install Voila - https://github.com/QuantStack/voila
- Clone this repo -
git clone https://github.com/goelakash/Hyperparameter-Tuning-With-Voila cdin the local clone of the repo and run
voila MNIST_widgets.ipynb
Here's what the dashboard finally looks like:
