Software for unsupervised deep architectures

Get uncomplicated access to unsupervised deep neural networks, from building their architecture to their training and evaluation

Ruta is based in the well known open source deep learning library Keras and its R interface. It has been developed to work with the TensorFlow backend. In order to install these dependencies you will need the Python interpreter as well, and you can install them via the Python package manager *pip* or possibly your distro’s package manager if you are running Linux.

Otherwise, you can follow the official installation guides:

```
# Just get Ruta from the CRAN
install.packages("ruta")
# Or get the latest development version from GitHub
devtools::install_github("fdavidcl/ruta")
```

All R dependencies will be automatically installed. These include the Keras R interface and `purrr`

. For convenience we also recommend installing and loading either `magrittr`

or `purrr`

, so that the pipe operator `%>%`

is available.

The easiest way to start working with Ruta is to use the `autoencode()`

function. It allows for selecting a type of autoencoder and transforming the feature space of a data set onto another one with some desirable properties depending on the chosen type.

`iris[, 1:4] %>% as.matrix %>% autoencode(2, type = "denoising")`

You can learn more about different variants of autoencoders by reading *A practical tutorial on autoencoders for nonlinear feature fusion*.

Ruta provides the functionality to build diverse neural architectures (see `autoencoder()`

), train them as autoencoders (see `train()`

) and perform different tasks with the resulting models (see `reconstruct()`

), including evaluation (see `evaluate_mean_squared_error()`

). The following is a basic example of a natural pipeline with an autoencoder:

```
library(ruta)
library(purrr)
# Shuffle and normalize dataset
x <- iris[, 1:4] %>% sample %>% as.matrix %>% scale
x_train <- x[1:100, ]
x_test <- x[101:150, ]
autoencoder(
input() + dense(256) + dense(36, "tanh") + dense(256) + output("sigmoid"),
loss = "mean_squared_error"
) %>%
make_contractive(weight = 1e-4) %>%
train(x_train, epochs = 40) %>%
evaluate_mean_squared_error(x_test)
```

For more details, see other examples and the documentation.