memory_layer: A 'torch::nn_module()' Representing a Memory Layer
In shapr: Prediction Explanation with Dependence-Aware Shapley Values

View source: R/approach_vaeac_torch_modules.R

memory_layer

R Documentation

A `torch::nn_module()` Representing a Memory Layer

Description

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.

Usage

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.