I am creating a tool for predicting the time and cost of software projects based on past data. The tool uses a neural network to do this and so far, the results are promising, but I think I can do a lot more optimisation just by changing the properties of the network. There don't seem to be any rules or even many best-practices when it comes to these settings so if anyone with experience could help me I would greatly appreciate it.
The input data is made up of a series of integers that could go up as high as the user wants to go, but most will be under 100,000 I would have thought. Some will be as low as 1. They are details like number of people on a project and the cost of a project, as well as details about database entities and use cases.
There are 10 inputs in total and 2 outputs (the time and cost). I am using Resilient Propagation to train the network. Currently it has: 10 input nodes, 1 hidden layer with 5 nodes and 2 output nodes. I am training to get under a 5% error rate.
The algorithm must run on a webserver so I have put in a measure to stop training when it looks like it isn't going anywhere. This is set to 10,000 training iterations.
Currently, when I try to train it with some data that is a bit varied, but well within the limits of what we expect users to put into it, it takes a long time to train, hitting the 10,000 iteration limit over and over again.
This is the first time I have used a neural network and I don't really know what to expect. If you could give me some hints on what sort of settings I should be using for the network and for the iteration limit I would greatly appreciate it.
Thank you!
