define_early_stopping {tsLSTMx}

Function to define early stopping callback

Description

This function defines an early stopping callback for keras models.

Usage

define_early_stopping(n_patience)

Arguments

n_patience

Integer specifying the number of epochs with no improvement after which training will be stopped.

Value

A keras early stopping callback.

Examples

data <- data.frame(
  Date = as.Date(c("01-04-18", "02-04-18", "03-04-18", "04-04-18", "05-04-18",
                   "06-04-18", "07-04-18", "08-04-18", "09-04-18", "10-04-18",
                   "11-04-18", "12-04-18", "13-04-18", "14-04-18", "15-04-18",
                   "16-04-18", "17-04-18", "18-04-18", "19-04-18", "20-04-18"),
                 format = "%d-%m-%y"),
  A = c(0, 0, 4, 12, 20, 16, 16, 0, 12, 18, 12, 18, 18, 0, 0, 33, 31, 38, 76, 198)
)
check_and_format_data(data)
# Add a new column 'X' based on the values in the second column
data$X <- ifelse(data$A != 0, 1, 0)

result_embed <- embed_columns(data = data, n_lag = 2)
new_data <- result_embed$data_frame
embedded_colnames <- result_embed$column_names

result_split <- split_data(new_data = new_data, val_ratio = 0.1)
train_data <- result_split$train_data
validation_data <- result_split$validation_data
train_data <- result_split$train_data
validation_data <- result_split$validation_data
embedded_colnames <- result_embed$column_names
numeric_matrices <- convert_to_numeric_matrices(train_data = train_data,
                                                validation_data = validation_data,
                                                embedded_colnames = embedded_colnames)
X_train <- numeric_matrices$X_train
y_train <- numeric_matrices$y_train
X_val <- numeric_matrices$X_val
y_val <- numeric_matrices$y_val

#' initialize_tensorflow()

X_train <- numeric_matrices$X_train
X_val <- numeric_matrices$X_val
reshaped_data <- reshape_for_lstm(X_train = X_train, X_val = X_val)
X_train <- reshaped_data$X_train
X_val <- reshaped_data$X_val
X_train <- reshaped_data$X_train
y_train <- numeric_matrices$y_train
X_val <- reshaped_data$X_val
y_val <- numeric_matrices$y_val
tf <- reticulate::import("tensorflow")
tensors <- convert_to_tensors(X_train = X_train, y_train = y_train, X_val = X_val, y_val = y_val)
X_train <- tensors$X_train
y_train <- tensors$y_train
X_val <- tensors$X_val
y_val <- tensors$y_val
n_patience <- 50
early_stopping <- define_early_stopping(n_patience = n_patience)