---hml_logit ---

Generate heaan.sdk.ml logistic model

hml_logit(
  context,
  unit_shape,
  num_feature,
  classes,
  initializer = "kaiming_he",
  path = NULL
)

Arguments

context:

(Context) - Context for HE.

unit_shape:

(integer vector) - Unit encoding shape of matrix.

num_feature:

(integer) - Number of features of the model.

classes:

(int vector) - List of class labels, starts with 0.

initializer:

(Optional: string, default "kaiming_he") - Initializer for the model parameter

path:

(Optional: string) - path of the dataset.

Value

logit model, path, nepoch, thetha.

Examples

if (FALSE) {
params <- heaan_sdk.HEParameter("FGb")
context <- heaan_sdk.Context(
     params,
     key_dir_path = key_dir_path,
     load_keys = "all",
     generate_keys = TRUE)
library(caret)
data(iris)
set.seed(34)
trainIndex <- createDataPartition(iris$Species,
             times = 1, p = 0.8, list = FALSE)
X_train <- iris[trainIndex, 1:4]
X_test <- iris[-trainIndex, 1:4]
y_train <- as.integer(iris[trainIndex, 5]) - 1
y_test <- as.integer(iris[-trainIndex, 5])- 1
classes <- c(0, 1, 2)
num_feature <- ncol(X_train)
batch_size <- 128
unit_shape <- (as.integer(c(batch_size,
             floor(py_to_r(context$num_slots) / batch_size))))
model <- hml_logit(
             context,
             unit_shape,
             classes,
             path = model_path)
}