Reference

Neural network architecture This set of functions provide the necessary functionality to define the neural architectures of autoencoders, by connecting layers of units.
input()	Create an input layer
dense()	Create a fully-connected neural layer
variational_block()	Create a variational block of layers
conv()	Create a convolutional layer
output()	Create an output layer
dropout()	Dropout layer
layer_keras()	Custom layer from Keras
`+`(<ruta_network>) c(<ruta_network>)	Add layers to a network/Join networks
`[`(<ruta_network>)	Access subnetworks of a network
plot(<ruta_network>)	Draw a neural network
new_layer()	Layer wrapper constructor
new_network()	Sequential network constructor
as_network()	Coercion to ruta_network
encoding_index()	Get the index of the encoding
Autoencoder and variants These functions allow to create and customize autoencoder learners.
autoencoder()	Create an autoencoder learner
autoencoder_contractive()	Create a contractive autoencoder
autoencoder_denoising()	Create a denoising autoencoder
autoencoder_robust()	Create a robust autoencoder
autoencoder_sparse()	Sparse autoencoder
autoencoder_variational()	Build a variational autoencoder
add_weight_decay()	Add weight decay to any autoencoder
weight_decay()	Weight decay
make_contractive()	Add contractive behavior to any autoencoder
make_denoising()	Add denoising behavior to any autoencoder
make_robust()	Add robust behavior to any autoencoder
make_sparse()	Add sparsity regularization to an autoencoder
is_contractive()	Detect whether an autoencoder is contractive
is_denoising()	Detect whether an autoencoder is denoising
is_robust()	Detect whether an autoencoder is robust
is_sparse()	Detect whether an autoencoder is sparse
is_variational()	Detect whether an autoencoder is variational
sparsity()	Sparsity regularization
new_autoencoder()	Create an autoencoder learner
Loss functions These functions define different objective functions which an autoencoder may optimize. Along with these, one may use any loss defined in Keras (such as "binary_crossentropy" or "mean_squared_error").
contraction()	Contractive loss
correntropy()	Correntropy loss
loss_variational()	Variational loss
as_loss()	Coercion to ruta_loss
Model training The following functions allow to train an autoencoder with input data.
autoencode()	Automatically compute an encoding of a data matrix
apply_filter()	Apply filters
configure()	Configure a learner object with the associated Keras objects
to_keras()	Convert a Ruta object onto Keras objects and functions
to_keras(<ruta_autoencoder>) to_keras(<ruta_autoencoder_variational>)	Extract Keras models from an autoencoder wrapper
to_keras(<ruta_filter>)	Get a Keras generator from a data filter
to_keras(<ruta_layer_input>) to_keras(<ruta_layer_dense>) to_keras(<ruta_layer_conv>) to_keras(<ruta_layer_custom>)	Convert Ruta layers onto Keras layers
to_keras(<ruta_layer_variational>)	Obtain a Keras block of layers for the variational autoencoder
to_keras(<ruta_loss_contraction>) to_keras(<ruta_loss_correntropy>) to_keras(<ruta_loss_variational>) to_keras(<ruta_loss_named>)	Obtain a Keras loss
to_keras(<ruta_network>)	Build a Keras network
to_keras(<ruta_sparsity>)	Translate sparsity regularization to Keras regularizer
to_keras(<ruta_weight_decay>)	Obtain a Keras weight decay
train()	Train a learner object with data
is_trained()	Detect trained models
Model evaluation Evaluation metrics for trained models.
evaluate_mean_squared_error() evaluate_mean_absolute_error() evaluate_binary_crossentropy() evaluate_binary_accuracy() evaluate_kullback_leibler_divergence()	Evaluation metrics
evaluation_metric()	Custom evaluation metrics
Tasks for trained models The following functions can be applied when an autoencoder has been trained, in order to transform data from the input space onto the latent space and viceversa.
encode()	Retrieve encoding of data
decode()	Retrieve decoding of encoded data
reconstruct() predict(<ruta_autoencoder>)	Retrieve reconstructions for input data
generate()	Generate samples from a generative model
save_as() load_from()	Save and load Ruta models
Noise generators These objects act as input filters which generate some noise into the training inputs when fitting denoising autoencoders.
noise()	Noise generator
noise_cauchy()	Additive Cauchy noise
noise_gaussian()	Additive Gaussian noise
noise_ones()	Filter to add ones noise
noise_saltpepper()	Filter to add salt-and-pepper noise
noise_zeros()	Filter to add zero noise
Keras conversions These are internal functions which convert Ruta wrapper objects into Keras objects and functions.
to_keras()	Convert a Ruta object onto Keras objects and functions
to_keras(<ruta_autoencoder>) to_keras(<ruta_autoencoder_variational>)	Extract Keras models from an autoencoder wrapper
to_keras(<ruta_filter>)	Get a Keras generator from a data filter
to_keras(<ruta_layer_input>) to_keras(<ruta_layer_dense>) to_keras(<ruta_layer_conv>) to_keras(<ruta_layer_custom>)	Convert Ruta layers onto Keras layers
to_keras(<ruta_layer_variational>)	Obtain a Keras block of layers for the variational autoencoder
to_keras(<ruta_loss_contraction>) to_keras(<ruta_loss_correntropy>) to_keras(<ruta_loss_variational>) to_keras(<ruta_loss_named>)	Obtain a Keras loss
to_keras(<ruta_network>)	Build a Keras network
to_keras(<ruta_sparsity>)	Translate sparsity regularization to Keras regularizer
to_keras(<ruta_weight_decay>)	Obtain a Keras weight decay
Other methods Some methods for R generics.
print(<ruta_autoencoder>) print(<ruta_loss_named>) print(<ruta_loss>) print(<ruta_network>)	Inspect Ruta objects