In a one-to-one architecture, a single input is mapped to a single output, and the output from the time step t is fed as an input to the next time step. We have already seen this architecture in the last section for generating songs using RNNs.

For instance, for a text generation task, we take the output generated from a current time step and feed it as the input to the next time step to generate the next word. This architecture is also widely used in stock market predictions.

The following figure shows the one-to-one RNN architecture. As you can see, output predicted at the time step t is sent as the input to the next time step: