Contents
Why does the prediction or the output of neural network does not change?
If the error curve on the training set separates itself too much from the error curve on the test set, then it means over-specialization (i.e., instead of coming up with a general solution, the ANN picks up a particular solution that works for the training set but not for the test set).
What should the RMS of a neural network be?
An RMS of 0.1 is fine at first. Particularly if you get it after about the same number of epochs whatever the training set “chosen”. I mean, you should split the full set of your data into the 3 sets mentioned earlier many times, by “chosing” randomly each time the training exemplars that are assigned to each of the 3 sets.
Why do I get different results each time in machine learning?
Differences in the development environment, such as software versions and CPU type, can cause rounding error differences in predictions and model evaluations. Let’s get started. Why Do I Get Different Results Each Time in Machine Learning?
As for the number of hidden layer units, try use the less possible (start with 5, for instance), and allow for more if the network is unable to learn the problem. If the problem requires very complicated nonlinear separations, it may be necessary that you introduce a 2nd hidden layer, but you need to try first with just 1 hidden layer.
When to do data normalization in neural network?
Data normalization (if necessary) should be carried out on the full set of your data at once, before you decide which part of your data will be training data and which part of your data will be cross-validation data.
Why is my neural network not working properly?
Shuffle the dataset If your dataset hasn’t been shuffled and has a particular order to it (ordered by label) this could negatively impact the learning. Shuffle your dataset to avoid this. Make sure you are shuffling input and labels together.