layer_node {deepregression} | R Documentation |
NODE/ODTs Layer
Description
NODE/ODTs Layer
Usage
layer_node(
name,
units,
n_layers = 1L,
n_trees = 1L,
tree_depth = 1L,
threshold_init_beta = 1
)
Arguments
name |
name of the layer |
units |
number of output dimensions, for regression and binary classification: 1, for mc-classification simply the number of classes |
n_layers |
number of layers consisting of ODTs in NODE |
n_trees |
number of trees per layer |
tree_depth |
depth of tree per layer |
threshold_init_beta |
parameter(s) for Beta-distribution used for initializing feature thresholds |
Value
layer/model object
Examples
n <- 1000
data_regr <- data.frame(matrix(rnorm(4 * n), c(n, 4)))
colnames(data_regr) <- c("x0", "x1", "x2", "x3")
y_regr <- rnorm(n) + data_regr$x0^2 + data_regr$x1 +
data_regr$x2*data_regr$x3 + data_regr$x2 + data_regr$x3
library(deepregression)
formula_node <- ~ node(x1, x2, x3, x0, n_trees = 2, n_layers = 2, tree_depth = 2)
mod_node_regr <- deepregression(
list_of_formulas = list(loc = formula_node, scale = ~ 1),
data = data_regr,
y = y_regr
)
if(!is.null(mod_node_regr)){
mod_node_regr %>% fit(epochs = 15, batch_size = 64, verbose = TRUE,
validation_split = 0.1, early_stopping = TRUE)
mod_node_regr %>% predict()
}
[Package deepregression version 2.2.0 Index]