Labels -1, 0 and 1 for classification in Tensorflow

Question

I am trying to write a model that outputs a vector of length N consisting of labels -1,0 and 1. Each of the labels depicts one of three decisions for the system participants (wireless devices). So the vector depicts a system state that is then passed on to an optimization problem in the next step. Due to the fix problem formulation that is awaiting the output vector a selection of 0,1 and 2 instead is not possible.

After coming across this tanh function to supply the -1,0 and 1 values: 1.5 * backend.tanh(alpha * x) + 0.5 * (backend.tanh(-(3 / alpha) * x)) from here, I was wondering how exactly this output layer and the penultimate layer can be built to suply this vector of labels {-1,0,1}. I tried using the above function in the output layer in a simple Iris classificator. But this resulted in terrible accuracy compared to the one achieved with 0,1,2 and softmax output layer.

Thanks in advance, with kind regards, Yuka

Answer 1

It doesn't seem like the outputs are actually "numerically related", for lack of a better term. Meaning, the labels could just as well be "left", "right", "up". So I think your best bet is to have 3 output nodes in the final layer, with softmax activation function, with each of the three nodes representing each of the three labels, using a Cross entropy loss function.

If your training data currently has the target as -1/0/1, you should one-hot encode it so that each target is a vector of length 3. So label 0 might be [0,1,0]