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.

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.

Author

Lars Henry Berge Olsen

Examples

if (FALSE) { # \dontrun{
memory_layer_env <- new.env()
net1 <- torch::nn_sequential(
  memory_layer("#1", shared_env = memory_layer_env),
  memory_layer("#0.1", shared_env = memory_layer_env),
  torch::nn_linear(512, 256),
  torch::nn_leaky_relu(), # Here add cannot be TRUE because the dimensions mismatch
  memory_layer("#0.1", shared_env = memory_layer_env, output = TRUE, add = FALSE),
  torch::nn_linear(768, 256),
  # the dimension after the concatenation with skip-connection is 512 + 256 = 768
)
net2 <- torch::nn_equential(
  torch::nn_linear(512, 512),
  memory_layer("#1", shared_env = memory_layer_env, output = TRUE, add = TRUE),
  ...
)
# Here a and c must be of correct dimensions, e.g., a = torch::torch_ones(1,512).
b <- net1(a)
d <- net2(c) # net2 must be called after net1, otherwise tensor '#1' will not be in storage.
} # }