Menu
I am looking for some relatively simple data sets for testing and comparing different training methods for artificial neural networks. I would like data that won't take too much pre-processing to turn it into my input format of a list of inputs and outputs (normalized to 0-1). Any links appreciated.
622
A validation data set is a data-set of examples used to tune the hyperparameters (i.e. the architecture) of a classifier. It is sometimes also called the development set or the "dev set". An example of a hyperparameter for artificial neural networks includes the number of hidden units in each layer.
According to Yaser S. Abu-Mostafa(Professor of Electrical Engineering and Computer Science) to get a proper result you must have data for at-least 10 times the degree of freedom. example for a neural network which has 3 weights you should have 30 data points.
Fitting a neural network involves using a training dataset to update the model weights to create a good mapping of inputs to outputs.
https://archive.ics.uci.edu/ml is the University of California Irvine repository of machine learning datasets. It's a really great resource, and I believe that they are all in CSV files.
197
Why not try something simple like the sin function as the training data? Since you are comparing the training methods and don't really care about what you are training the network for, it should work and be easy to generate the training data.
Train the network using sin(x) where x is the input and the output is the value of the function. An added benefit in your case is that the absolute value of the result is already in the range 0-1. It would equally work with other mathematical functions.
45
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
