Using ConvNetSharp With Feature Based Data
ConvNetSharp which is descended from ConvNetJs is a library which enables you to use Neural Networks in .NET without the need to call out to other languages or services.
ConvNetSharp also has GPU support which makes it a good option for training networks.
Since much of the interest (and as a result the guides) around Neural Networks focuses on their utility in image analysis, it's slightly unclear how to apply these libraries to numeric and categorical features you may be used to using for SVMs or other machine learning methods.
The aim of this blog post is to note how to acheive this.
Let's take the example of some data observed in a scientific experiment. Perhaps we are trying to predict which snails make good racing snails.
Our data set looks like this:
Age Stalk Height Shell Diameter Shell Color Good Snail?
1 0.52 7.6 Light Brown No
1.2 0.74 6.75 Brown Yes
1.16 0.73 7.01 Grey Yes
etc...
ConvNetSharp uses the concept of Volume
s to deal with input and classification data. A Volume is a 4 dimensional shape containing data.
The 4 dimensions are: