params <-
list(eval = FALSE)

## ----include=FALSE------------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = params$eval
)

## -----------------------------------------------------------------------------
# library(LBBNN)
# library(ggplot2)
# library(torch)

## -----------------------------------------------------------------------------
# loaders <- get_dataloaders(Raisin_Dataset, train_proportion = 0.8,
#                            train_batch_size = 720, test_batch_size = 180)
# train_loader <- loaders$train_loader
# test_loader  <- loaders$test_loader

## -----------------------------------------------------------------------------
# problem <- "binary classification"
# sizes <- c(7, 5, 5, 1)
# inclusion_priors <- c(0.5, 0.5, 0.5)
# stds <- c(1, 1, 1)
# inclusion_inits <- matrix(rep(c(-10, 15), 3), nrow = 2, ncol = 3)
# device <- "cpu"
# torch_manual_seed(0)
# model_input_skip <- lbbnn_net(problem_type = problem, sizes = sizes,
#                               prior = inclusion_priors,
#                               inclusion_inits = inclusion_inits,
#                               input_skip = TRUE, std = stds,
#                               flow = FALSE, device = device)

## -----------------------------------------------------------------------------
# results_input_skip <- train_lbbnn(epochs = 50, LBBNN = model_input_skip,
#                                   lr = 0.005, train_dl = train_loader,
#                                   device = device)

## -----------------------------------------------------------------------------
# validate_lbbnn(LBBNN = model_input_skip, num_samples = 100,
#                test_dl = test_loader, device = device)