memory_layer function

A torch::nn_module() Representing a Memory Layer

The layer is used to make skip-connections inside a torch::nn_sequential() network or between several torch::nn_sequential() networks without unnecessary code complication.

memory_layer(id, shared_env, output = FALSE, add = FALSE, verbose = FALSE)

Arguments

• id: A unique id to use as a key in the storage list.
• shared_env: A shared environment for all instances of memory_layer where the inputs are stored.
• output: Boolean variable indicating if the memory layer is to store input in storage or extract from storage.
• add: Boolean variable indicating if the extracted value are to be added or concatenated to the input. Only applicable when output = TRUE.
• verbose: Boolean variable indicating if we want to give printouts to the user.

Details

If output = FALSE, this layer stores its input in the shared_env with the key id and then passes the input to the next layer. I.e., when memory layer is used in the masked encoder. If output = TRUE, this layer takes stored tensor from the storage. I.e., when memory layer is used in the decoder. If add = TRUE, it returns sum of the stored vector and an input, otherwise it returns their concatenation. If the tensor with specified id is not in storage when the layer with output = TRUE is called, it would cause an exception.

Author(s)

Lars Henry Berge Olsen