How to get an unbounded continuous output from a neural network?
You are actually in the correct direction
Option1:
you need to normalize the temperatures first and then fed it to the model let say your temperature ranges from [-100,100] so convert it into a scale of [-1,1] then use this scaled version of temp in your target variable.
At the time of prediction just inverse transform the output and you will get your desired result.
Option2:
You create a regression kind of neural network and don't apply any activation function to the output layer (means no bonds for values it could be +ve or -ve).
In this case you are not required to normalize the values.
Sample NN Spec:
Input Layer==> # neurons one per feature
Hidden Layer==>relu/selu as activation function| # of neurons/Hidden layers is as per your requirement
Output Layer==> 1 neuron/ no Activation function required
I am currently building a neural network to predict features such as temperature. So the output for this could be a positive or negative value. I am normalizing my input data and using the tanh activation function in each hidden layer.
Should I use a linear activation function for the output layer to get an unbounded continuous output OR should I use tanh for the output layer and then inverse normalize the output? Could someone explain this I don't think my understanding of this is correct.