+.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 |
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 |