add_weight_decay | Add weight decay to any autoencoder |
apply_filter | 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 |
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 | Evaluation metrics |
evaluation_metric | Custom evaluation metrics |
generate | 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 |
join-networks | Add layers to a network/Join networks |
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-methods | Inspect Ruta objects |
reconstruct | Retrieve reconstructions for input data |
save_as | Save and load Ruta models |
sparsity | Sparsity regularization |
sub-.ruta_network | Access subnetworks of a network |
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... |
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.ruta_autoencoder | Train a learner object with data |
variational_block | Create a variational block of layers |
weight_decay | Weight decay |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.