I have trained a ResNet (convolutional neural network) with a global average pooling layer at the end and a softmax layer with 3 units for the classification of 3 classes. As input to the network I have images with 3 color channels.
Now I would like to add one additional one-dimensional input (just one number per image). I see the possibility to add this input to the vector which I get from the global average pooling layer. The drawback is that this vector is then directly fed into the softmax layer.
Another possibility would be to append the additional input to the result of the average pooling layer but then adding some fully-connected layers (dense layers) after the global average pooling layer. Unfortunately, adding such fully-connected layers makes the performance much worse.
Are there any other possibilities to add this additional input or which one is the best way?