ModelCheckpoint | R Documentation |
Save the model after every epoch.
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
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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