ModelCheckpoint: Save the model after every epoch.
In YTLogos/kerasR: R Interface to the Keras Deep Learning Library

Description Usage Arguments Author(s) References See Also Examples

Save the model after every epoch.

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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)
}