The example inspired me to change the code in section 14.3.1 (A more realistic scenario) to use the optim_lbfgs optimizer.
fitting produces several errors, which are reproduced below.
Epoch 1/20
Error in `FUN()`:
! Error while calling callback with class <metrics_callback/LuzCallback/R6> at
on_train_batch_end.
Caused by error in `FUN()`:
! Error when evaluating update for metric with abbrev "MAE" and class <LuzMetric/R6>
ℹ The error happened at iter 1 of epoch 1.
ℹ The model was in training mode.
Caused by error in `torch_tensor_cpp()`:
! R type not handled
#! /usr/bin/env Rscript
library(torch)
library(luz)
n <- 1000
d_in <- 3
x <- torch_randn(n, d_in)
coefs <- c(0.2, -1.3, -0.5)
y <- x$matmul(coefs)$unsqueeze(2) + torch_randn(n, 1)
ds <- tensor_dataset(x, y)
dl <- dataloader(ds, batch_size = 100, shuffle = TRUE)
d_hidden <- 32
d_out <- 1
net <- nn_module(
initialize = function(d_in, d_hidden, d_out) {
self$net <- nn_sequential(
nn_linear(d_in, d_hidden),
nn_relu(),
nn_linear(d_hidden, d_out)
)
},
forward = function(x) {
self$net(x)
},
step = function() {
if (ctx$training) {
closure <- function() {
pred <- ctx$model(ctx$input)
loss <- ctx$loss_fn(pred, ctx$target)
loss$backward()
loss
}
ctx$loss <- ctx$opt$step(closure)
} else {
pred <- ctx$model(ctx$input)
ctx$loss <- ctx$loss_fn(pred, ctx$target)
}
}
)
train_ids <- sample(
1:length(ds),
size = 0.6 * length(ds))
valid_ids <- sample(
setdiff(1:length(ds), train_ids),
size = 0.2 * length(ds)
)
test_ids <- setdiff(1:length(ds), union(train_ids, valid_ids))
train_ds <- dataset_subset(ds, indices = train_ids)
valid_ds <- dataset_subset(ds, indices = valid_ids)
test_ds <- dataset_subset(ds, indices = test_ids)
train_dl <- dataloader(train_ds, batch_size = 100, shuffle = TRUE)
valid_dl <- dataloader(valid_ds, batch_size = 100)
test_dl <- dataloader(test_ds, batch_size = 100)
fitted <- net |>
setup(
loss = nn_mse_loss(),
optimizer = optim_lbfgs,
# metrics = list(luz_metric_mae())
) |>
set_hparams(d_in = d_in, d_hidden = d_hidden, d_out = d_out) |>
set_opt_hparams(line_search_fn = "strong_wolfe") |>
fit(train_dl, epochs = 20, valid_data = valid_dl)
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