I have a neural network with 2 inputs and one output, like so:
input | output ____________________ a | b | c 5.15 |3.17 | 0.0607 4.61 |2.91 | 0.1551
I have 75 samples and I am using 50 for training and 25 for testing.
However, I feel that the training samples are not enough. Because I can't provide more real samples (due to time limitation), I would like to train the network using fake data:
For example, I know that the range for the
a parameter is from 3 to 14, and that the
b parameter is ~65% of the
a parameter. I also know that
c is a number between 0 and 1 and that it increases when a & b increase.
So, what I would like to do is to generate some data using the above restrictions (about 20 samples). For example, assume
a = 13 ,
b = 8 and
c= 0.95, and train the network with these samples before training it with the real samples.
Has anybody studied the effect of doing this on the neural network? Is it possible to know if the effect will be better or worse on the networks? Are there any recommendations/guidelines if I want to do this?