Stop training when a monitored quantity has stopped improving.
Stop training when a monitored quantity has stopped improving.
EarlyStopping(monitor = "val_loss", min_delta = 0, patience = 0, verbose = 0, mode = "auto")
monitor |
quantity to be monitored. |
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, 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. |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other callbacks: CSVLogger,
ModelCheckpoint,
ReduceLROnPlateau,
TensorBoard
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)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.