How to avoid getting negative values when training a neural network?

Is there anyway to constrain the network results when we train a feed forward neural network in Matlab?

I am trying to train a supervised feed forward neural network with 100,000 observations. I have 5 continues variables and 3 countinues responses (labels). All my values are positive (labels and variables). However, when I train the network, sometimes it predicts negative results no matter what architecture I use. Negative results does not have any physical meaning and should not apear. Is there anyway to constrain the network? I also used reLU activation function for the last layer but the network cannot generalize well.

Thanks

Answer 1

[Mostafa Nakhaei]

I found the answer for my problem. The main reason for getting negative results after I trained and tested the dataset with positive numbers was that the distribution of new dataset was different from those of train and test samples. They had more noise. In my case, the solution was not to change the activation functions of the last layer (it leaded to physically meaningless results) but to add some syntatic random noise to my dataset. This robusted the model against the noise.

Thanks

Mostafa

Answer 2

[Greg Heath]

Use a sigmoid for the output layer.

Hope this helps

THANK YOU FOR FORMALLY ACCEPTING MY ANSWER

GREG

[Mostafa Nakhaei]

Thanks Greg for the response.

This is the regression problem and also I guess sigmoid would give negative results as well.r