This variational block consists in two dense layers which take as input the previous layer
and a sampling layer. More specifically, these layers aim to represent the mean and the
log variance of the learned distribution in a variational autoencoder.

variational_block(units, epsilon_std = 1, seed = NULL)

## Arguments

units |
Number of units |

epsilon_std |
Standard deviation for the normal distribution used for sampling |

seed |
A seed for the random number generator. **Setting a seed is required if you
want to save the model and be able to load it correctly** |

## Value

A construct with class `"ruta_layer"`

## See also

## Examples

variational_block(3)

#> Network structure:
#> variational(3 units)