EarlyStopping: Stop training when a monitored quantity has stopped...
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Author(s) References See Also Examples

Stop training when a monitored quantity has stopped improving.

1 2	EarlyStopping(monitor = "val_loss", min_delta = 0, patience = 0, verbose = 0, mode = "auto")

`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.

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)
}