# What are the symptoms of having too few training samples when training a neural network?

I have a small data set with about 400 observations and 5 features. I want to train a neural network using the data. I use cross-validation and early stopping. I suspect that the data set size is too small. However, it's just a feeling.

What are the symptoms of having too few training samples? How can I check if the amount of data is sufficient?

You might argue that a bad model fit is a symptom but how do I know, if the model fits the data bad? Is a RMSE of 0.6 bad? Is a R2 of 0.4 bad?

As a rule of thumb, make sure that number of training Samples is $$\geq$$ 10*(VC-dimension of the model)
VC dimension ~ number of effective tunable parameters $$\leq$$ total number of weights in your neural net