EarlyStopping | R Documentation |
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. See |
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 |
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) }
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