# Getting started with LBBNN

## Introduction

LBBNN implements Latent Bayesian Binary Neural Networks in R using the torch framework. This vignette walks through basic usage: data preparation, model definition, training, validation, and visualization.

## Setup

    library(LBBNN)
    library(ggplot2)
    library(torch)

## Data loaders

    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

## Define the model

    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)

## Train

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

## Validate

    validate_LBBNN(LBBNN = model_input_skip, num_samples = 100,
                   test_dl = test_loader, device = device)

## Plot structure and explanations

    LBBNN_plot(model_input_skip, layer_spacing = 1, neuron_spacing = 1,
               vertex_size = 15, edge_width = 0.5)

    x <- torch::dataloader_next(torch::dataloader_make_iter(train_loader))[[1]]
    inds <- sample.int(dim(x)[1], 1)
    data <- x[inds,]
    plot_local_explanations_gradient(model_input_skip, data, num_samples = 100, device = device)

Note: All chunks in this vignette are non-evaluated by default to ensure fast builds and avoid backend constraints on CRAN/CI. You can set `eval=TRUE` locally.