# Recurrent Neural Networks

## Description

This module is used to initiate a Recurrent Neural Networks estimator based on SmartPredict library.

## Parameters

The Recurrent Neural Networks module&#x20;

## Optimization parameters

The available optimizers for the RNN are:&#x20;

* **adam**&#x20;
* **rmsprop**&#x20;
* **adagrad**&#x20;
* **adamax**
* &#x20;**sgd**

Other optimization parameters can also be set: **Learning rate , Beta Gradient, Clipping by Norm or by Value, Epsilon Momentum, Rho.**

### Loss function

The loss function can be:

* **binary\_xentropy**
* &#x20;**categorical\_xentropy**&#x20;
* **sparse\_categorical \_xentropy**&#x20;
* **hinge**&#x20;
* **mae**&#x20;
* **cosine**

![The Recurrent Neural Network module contains a range of loss functions .](https://1833277725-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lyc1OXsKqB2S62LxsOR%2F-M1URPnN771EjHE_1Cp-%2F-M1UWaM8A4Nl7q0P55tK%2Fimage.png?alt=media\&token=53e10dd6-f1d2-4bd6-8d5e-61ec32967079)

The module also contains a range of parameters such as : layer cells, timestamp, hidden recurrent units...&#x20;
