- Create 6 labels (3 * 2) and train the network for classification on this problem. Since the number of instances are equal, you should get decent result but ensure to not let the model overfit.
- You can have common layers upto a point and then split the network into two halves, one with output of 2 and other with output of 3. You can refer to the following example. Assemble Multiple-Output Network for Prediction
- You can also have two seperate networks for both the predictions, but this is just the brute force way of doing the above.
how do I set my fully connected layer to be (3 or 2) classification output?
5 views (last 30 days)
Im working with pretrained network.
Currently, I have 3 age group (17-20, 21-40, 41-60) and another one is (female , male). My question is how to change the fully connected layer for this type of classification.
Currently I had 6 classes, 3 age group for each gender. Therefore, my classification output is 6 classes instead. Is this the correct way to classify?
Vineet Joshi on 19 Jul 2021
It is not possible to have one layer with arbitary number of neurons (3 or 2) . Keeping this in mind, there are three ways to approach your problem.
Hope this was of some help.