train: Train

In brain-r/brain: Neural Network

Description

Train the brain.

Usage

train(brain, cost, log = NULL, print = TRUE)

train_input(brain, ..., scale = FALSE, data = NULL)

train_output(brain, ..., scale = FALSE, data = NULL)

train_data(brain, data)

train_opts(brain, rate = NULL, iterations = NULL, error = NULL,
  shuffle = NULL, cv = NULL)

cost_function(func)

get_training(brain)

get_log(brain)

Arguments

brain	An object of class brain as returned by brain.
cost	A cost function as returned by cost_function.
log	Frequency at which to log training error and elapsed time (iterations), can be retrieved with get_log.
print	Whether to print training error, iterations and elapsed time.
...	Bare column names from data.
scale	Set to TRUE to scale the data with balance.
data	A data.frame from where to select input and output (...).
rate	Learning rate to train the network. It can be a static rate (just a number), dynamic (an list of numbers, which will transition from one to the next one according to the number of iterations).
iterations	Maximum number of iterations.
error	Minimum error.
shuffle	If TRUE, the training set is shuffled after every iteration, this is useful for training data sequences which order is not meaningful to networks with context memory, like lstm.
cv	Set to TRUE to cross validate.
func	Training cost function, one of cross_entropy, mse (mean squared error), or binary.

Functions

• train: Train the brain.

• train_data: Add training data.

• train_opts: Pass training options.

• train_input, train_output: Pass training input and output based on data.

• cost_function: Returns a cost function to use as cost in train function.

• get_training: Returns training error, iterations and elapsed time.

Cost functions

• mse: Mean squared error

• binary: Binay

• cross_entropy: Cross Entropy

See Also

network to manually create layers, connections and resulting brain, dsr for distracted sequence recall.

Examples

df <- dplyr::tibble(
  x = c(0, 0, 1, 1),
  y = c(0, 1, 0, 1),
  z = c(0, 1, 1, 0)
)

br <- brain() %>%
  perceptron(c(2,3,1)) %>%
  train_data(df) %>%
  train_input(x, y) %>%
  train_output(z) %>%
  train(
    cost = cost_function("cross_entropy"),
    log = 10
  )

log <- get_log(br)
plot(log)

brain-r/brain documentation built on May 21, 2019, 4:05 a.m.