ReduceLROnPlateau: Reduce learning rate when a metric has stopped improving.
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Author(s) References See Also Examples

Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.

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ReduceLROnPlateau(monitor = "val_loss", factor = 0.1, patience = 10,
  verbose = 0, mode = "auto", epsilon = 1e-04, cooldown = 0,
  min_lr = 0)

`monitor`	quantity to be monitored.
`factor`	factor by which the learning rate will be reduced. new_lr = lr * factor
`patience`	number of epochs with no improvement after which learning rate will be reduced.
`verbose`	int. 0: quiet, 1: update messages.
`mode`	one of auto, min, max. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity.
`epsilon`	threshold for measuring the new optimum, to only focus on significant changes.
`cooldown`	number of epochs to wait before resuming normal operation after lr has been reduced.
`min_lr`	lower bound on the learning rate.

Taylor B. Arnold, taylor.arnold@acm.org

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

Other callbacks: CSVLogger, EarlyStopping, ModelCheckpoint, 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)
}