Design and train a neural network to accomplish some classification task. For this milestone, you will design and train a neural network to accomplish some classification task.

Design and train a neural network to accomplish some classification task.
For this milestone, you will design and train a neural network to accomplish some classification task.

Choose a data set
The UCI Machine Learning Archive hosts various data sets suitable for testing learning algorithms. I suggest clicking on “View ALL Data Sets” on the right side of the page. That provides a nice interface in which you can filter by data type or area of interest.

The data should be suitable for a classification task, not clustering, recommendations, or regression. Neural networks support both categorical and numerical data, you’ll just want to keep the number of attributes to less than 100, because we’ll have to tune the way each attribute is presented to the network.

When you click on the data set, you’ll see a description, citations, and details about the attributes. There are links near the top to the “Data Folder”, and there you’ll find a list of files ending in .data (the raw data) or .names (attribute descriptions).

Download the data and descriptions. I have a lot of experience with the Mushroom data, so I’ll explore that in this explanation – but you can choose something else for your project. For mushrooms, the .names file contains:

1. Title: Mushroom Database

2. Sources:
(a) Mushroom records drawn from The Audubon Society Field Guide to North
American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred
A. Knopf
(b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
(c) Date: 27 April 1987

3. Past Usage:
1. Schlimmer,J.S. (1987). Concept Acquisition Through Representational
Adjustment (Technical Report 87-19). Doctoral disseration, Department
of Information and Computer Science, University of California, Irvine.
— STAGGER: asymptoted to 95% classification accuracy after reviewing
1000 instances.

[etc.]
5. Number of Instances: 8124

6. Number of Attributes: 22 (all nominally valued)

7. Attribute Information: (classes: edible=e, poisonous=p)
1. cap-shape: bell=b,conical=c,convex=x,flat=f,
knobbed=k,sunken=s
2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s
3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r,
pink=p,purple=u,red=e,white=w,yellow=y

[etc.]
The .data file is a text file with comma-separated values (CSV), which can be imported easily into Excel or other spreadsheet applications:

p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g
e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m
p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u
e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
[etc.]
Design your network
Your next task is to design your neural network architecture: how many neurons in each layer, and how to map neuronal activations to and from the data set?

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