kerasR (version 0.8.1)

EarlyStopping: Stop training when a monitored quantity has stopped improving.

Description

Stop training when a monitored quantity has stopped improving.

Usage

EarlyStopping(monitor = "val_loss", min_delta = 0, patience = 0,
  verbose = 0, mode = "auto")

Arguments

monitor

quantity to be monitored. See keras_compile() for list of metrics

min_delta

minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.

patience

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

verbose

verbosity mode.

mode

one of auto, min, or max. In min mode, training will stop when the quantity monitored has stopped decreasing; in max mode it will stop when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity.

Author

Taylor B. Arnold,

References

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

See Also

Other callbacks: CSVLogger, ModelCheckpoint, ReduceLROnPlateau, TensorBoard

Examples

if(keras_available()) {
  X_train <- matrix(rnorm(100 * 10), nrow = 100)
  Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)

  mod <- Sequential()
  mod$add(Dense(units = 50, input_shape = dim(X_train)[2]))
  mod$add(Activation("relu"))
  mod$add(Dense(units = 3))
  mod$add(Activation("softmax"))
  keras_compile(mod,  loss = 'categorical_crossentropy', optimizer = RMSprop())

  callbacks <- list(CSVLogger(tempfile()),
                    EarlyStopping(),
                    ReduceLROnPlateau(),
                    TensorBoard(tempfile()))

  keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
            verbose = 0, callbacks = callbacks, validation_split = 0.2)
}