LBBNN (version 0.1.2)

LBBNN_plot: Function to plot an input skip structure after removing weights in non-active paths.

Description

Uses igraph to plot.

Usage

LBBNN_plot(
  model,
  layer_spacing = 1,
  neuron_spacing = 1,
  vertex_size = 10,
  label_size = 0.5,
  edge_width = 0.5,
  save_svg = NULL
)

Value

This function produces plots as a side effect and does not return a value.

Arguments

model

A trained LBBNN_Net model with input_skip enabled.

layer_spacing

numeric, spacing in between layers.

neuron_spacing

numeric, spacing between neurons within a layer.

vertex_size

numeric, size of the neurons.

label_size

numeric, size of the text within neurons.

edge_width

numeric, width of the edges connecting neurons.

save_svg

the path where the plot will be saved if save_svg is not null.

Examples

# \donttest{
sizes <- c(2,3,3,2)
problem <- 'multiclass classification'
inclusion_priors <- c(0.1,0.1,0.1)
std_priors <- c(1.0,1.0,1.0)
inclusion_inits <- matrix(rep(c(-10,10),3), nrow = 2, ncol = 3)
device <- 'cpu'
torch::torch_manual_seed(0)
model <- LBBNN_Net(problem_type = problem, sizes = sizes,
                   prior = inclusion_priors, inclusion_inits = inclusion_inits,
                   input_skip = TRUE, std = std_priors, flow = FALSE,
                   num_transforms = 2, dims = c(200,200), device = device)
model$compute_paths_input_skip()
LBBNN_plot(model, 1, 1, 14, 1)
# }