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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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.