Implementation of Unsupervised Neural Architectures

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Documentation for package ‘ruta’ version 1.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

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+.ruta_network	Add layers to a network/Join networks
add_weight_decay	Add weight decay to any autoencoder
apply_filter	Apply filters
apply_filter.ruta_noise_cauchy	Apply filters
apply_filter.ruta_noise_gaussian	Apply filters
apply_filter.ruta_noise_ones	Apply filters
apply_filter.ruta_noise_saltpepper	Apply filters
apply_filter.ruta_noise_zeros	Apply filters
as_loss	Coercion to ruta_loss
as_loss.character	Coercion to ruta_loss
as_loss.ruta_loss	Coercion to ruta_loss
as_network	Coercion to ruta_network
as_network.integer	Coercion to ruta_network
as_network.numeric	Coercion to ruta_network
as_network.ruta_layer	Coercion to ruta_network
as_network.ruta_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
c.ruta_network	Add layers to a network/Join networks
configure	Configure a learner object with the associated Keras objects
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_binary_accuracy	Evaluation metrics
evaluate_binary_crossentropy	Evaluation metrics
evaluate_kullback_leibler_divergence	Evaluation metrics
evaluate_mean_absolute_error	Evaluation metrics
evaluate_mean_squared_error	Evaluation metrics
evaluation_metric	Custom evaluation metrics
generate	Generate samples from a generative model
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
load_from	Save and load Ruta models
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
predict.ruta_autoencoder	Retrieve reconstructions for input data
print.ruta_autoencoder	Inspect Ruta objects
print.ruta_loss	Inspect Ruta objects
print.ruta_loss_named	Inspect Ruta objects
print.ruta_network	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_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_conv	Convert Ruta layers onto Keras layers
to_keras.ruta_layer_custom	Convert Ruta layers onto Keras layers
to_keras.ruta_layer_dense	Convert Ruta layers onto Keras layers
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_loss_correntropy	Obtain a Keras loss
to_keras.ruta_loss_named	Obtain a Keras loss
to_keras.ruta_loss_variational	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
train.ruta_autoencoder	Train a learner object with data
variational_block	Create a variational block of layers
weight_decay	Weight decay
[.ruta_network	Access subnetworks of a network