+.ruta_network          Add layers to a network/Join networks
[.ruta_network          Access subnetworks of a network
add_weight_decay        Add weight decay to any autoencoder
apply_filter.ruta_noise_zeros
                        Apply filters
as_loss                 Coercion to ruta_loss
as_network              Coercion to ruta_network
autoencode              Automatically compute an encoding of a data
                        matrix
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
contraction             Contractive loss
conv                    Create a convolutional layer
correntropy             Correntropy loss
decode                  Retrieve decoding of encoded data
dense                   Create a fully-connected neural layer
dropout                 Dropout layer
encode                  Retrieve encoding of data
encoding_index          Get the index of the encoding
evaluate_mean_squared_error
                        Evaluation metrics
evaluation_metric       Custom evaluation metrics
generate.ruta_autoencoder_variational
                        Generate samples from a generative model
input                   Create an input layer
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_trained              Detect trained models
is_variational          Detect whether an autoencoder is variational
layer_keras             Custom layer from Keras
loss_variational        Variational loss
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
new_autoencoder         Create an autoencoder learner
new_layer               Layer wrapper constructor
new_network             Sequential network constructor
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
output                  Create an output layer
plot.ruta_network       Draw a neural network
print.ruta_autoencoder
                        Inspect Ruta objects
reconstruct             Retrieve reconstructions for input data
save_as                 Save and load Ruta models
sparsity                Sparsity regularization
to_keras                Convert a Ruta object onto Keras objects and
                        functions
to_keras.ruta_autoencoder
                        Extract Keras models from an autoencoder
                        wrapper
to_keras.ruta_filter    Get a Keras generator from a data filter
to_keras.ruta_layer_input
                        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
                        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.ruta_autoencoder
                        Train a learner object with data
variational_block       Create a variational block of layers
weight_decay            Weight decay
