Description Usage Arguments Author(s) References See Also Examples
Save the model after every epoch.
1 2 3 | ModelCheckpoint(filepath, monitor = "val_loss", verbose = 0,
save_best_only = FALSE, save_weights_only = FALSE, mode = "auto",
period = 1)
|
filepath |
string, path to save the model file. |
monitor |
quantity to monitor. |
verbose |
verbosity mode, 0 or 1. |
save_best_only |
if save_best_only=True, the latest best model according to the quantity monitored will not be overwritten. |
save_weights_only |
if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (model.save(filepath)). |
mode |
one of auto, min, max. If save_best_only is True, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For val_acc, this should be max, for val_loss this should be min, etc. the direction is automatically inferred from the name of the monitored quantity. |
period |
Interval (number of epochs) between checkpoints. |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other callbacks: CSVLogger
,
EarlyStopping
,
ReduceLROnPlateau
,
TensorBoard
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | 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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