R Deep Learning Cookbook

How to do it...

1. The standard autoencoder code of TensorFlow can easily be extended to the sparse autoencoder module by updating the cost function. This section will introduce the autoencoder package of R, which comes with built-in functionality to run the sparse autoencoder:

### Setting-up parameternl<-3 N.hidden<-100 unit.type<-"logistic" lambda<-0.001 rho<-0.01 beta<-6 max.iterations<-2000 epsilon<-0.001 ### Running sparse autoencoderspe_ae_obj <- autoencode(X.train=trainData,  X.test = validData, nl=nl, N.hidden=N.hidden, unit.type=unit.type,lambda=lambda,beta=beta,              epsilon=epsilon,rho=rho,max.iterations=max.iterations, rescale.flag = T)

The major parameters in the autoencode functions are as follows:

• nl: This is the number of layers ...