You can do that directly using R's torch package which seems quite comprehensive at least for the basic tasks.
Neural networks
Here is an example of how to create nn.Sequential like this:
library(torch)
model <- nn_sequential(
nn_linear(D_in, H),
nn_relu(),
nn_linear(H, D_out)
)
Below is a custom nn_module (a.k.a. torch.nn.Module) which is a simple dense (torch.nn.Linear) layer (source):
library(torch)
# creates example tensors. x requires_grad = TRUE tells that
# we are going to take derivatives over it.
dense <- nn_module(
clasname = "dense",
# the initialize function tuns whenever we instantiate the model
initialize = function(in_features, out_features) {
# just for you to see when this function is called
cat("Calling initialize!")
# we use nn_parameter to indicate that those tensors are special
# and should be treated as parameters by `nn_module`.
self$w <- nn_parameter(torch_randn(in_features, out_features))
self$b <- nn_parameter(torch_zeros(out_features))
},
# this function is called whenever we call our model on input.
forward = function(x) {
cat("Calling forward!")
torch_mm(x, self$w) + self$b
}
)
model <- dense(3, 1)
Another example, using torch.nn.Linear layers to create a neural network this time (source):
two_layer_net <- nn_module(
"two_layer_net",
initialize = function(D_in, H, D_out) {
self$linear1 <- nn_linear(D_in, H)
self$linear2 <- nn_linear(H, D_out)
},
forward = function(x) {
x %>%
self$linear1() %>%
nnf_relu() %>%
self$linear2()
}
)
Also there are other resources like here (using flow control and weight sharing).
Other
Looking at the reference it seems most of the layers are already provided (didn't notice transformer layers at a quick glance, but this is minor).
As far as I can tell basic blocks for neural networks, their training etc. are in-place (even JIT so sharing between languages should be possible).
