# RNN Structure

## RNN Structure

The image below shows the structure of a simple RNN.

## ![](https://3732905555-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RGo4dZhdwCIHrC1%2F-M5-0TEuN77zAmTpQjIr%2F-M5-0WaYG8NZxFXp4Kf9%2FRNN.JPG?generation=1586990829516238\&alt=media)

An RNN maps every $$x^{\lt i\gt}$$ to a $$y^{\lt i\gt}$$, and while doing so, it uses information learned at previous timestamps. However, it does not use information from future timestamps.

$$a^{<t>} = g(W_{aa}a^{<t-1>}+W_{az}x^{<t>}+b_{a})$$

$$\hat{y}^{<t>}=g(W_{ya}a^{<t>}+b_{y})$$
