I am learning Deep RL following this tutorial: https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8

I understand everything but one detail:

This image shows the difference between a classic Q learning table and a DNN. It states that a Q table needs a state-action pair as input and outputs the corresponding Q value whereas a Deep Q network needs the state as feature input and outputs the Q value for each action that can be made in that state.

But shouldn't the state AND the action together be the input to the network and the network just outputs a single Q value?