callback_model_checkpoint() is used in conjunction with training using
model |> fit() to save a model or weights (in a checkpoint file) at some
interval, so the model or weights can be loaded later to continue the
training from the state saved.
A few options this callback provides include:
Whether to only keep the model that has achieved the "best performance" so far, or whether to save the model at the end of every epoch regardless of performance.
Definition of "best"; which quantity to monitor and whether it should be maximized or minimized.
The frequency it should save at. Currently, the callback supports saving at the end of every epoch, or after a fixed number of training batches.
Whether only weights are saved, or the whole model is saved.
callback_model_checkpoint(
filepath,
monitor = "val_loss",
verbose = 0L,
save_best_only = FALSE,
save_weights_only = FALSE,
mode = "auto",
save_freq = "epoch",
initial_value_threshold = NULL
)A Callback instance that can be passed to fit.keras.src.models.model.Model().
string, path to save the model file.
filepath can contain named formatting options,
which will be filled the value of epoch and keys in logs
(passed in on_epoch_end).
The filepath name needs to end with ".weights.h5" when
save_weights_only = TRUE or should end with ".keras" when
checkpoint saving the whole model (default).
For example:
if filepath is "{epoch:02d}-{val_loss:.2f}.keras", then the
model checkpoints will be saved with the epoch number and the
validation loss in the filename. The directory of the filepath
should not be reused by any other callbacks to avoid conflicts.
The metric name to monitor. Typically the metrics are set by
the model |> compile() method. Note:
Prefix the name with "val_" to monitor validation metrics.
Use "loss" or "val_loss" to monitor the model's total loss.
If you specify metrics as strings, like "accuracy", pass the
same string (with or without the "val_" prefix).
If you pass Metric objects (created by one of metric_*()), monitor should be set to
metric$name.
If you're not sure about the metric names you can check the
contents of the history$metrics list returned by
history <- model |> fit()
Multi-output models set additional prefixes on the metric names.
Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action.
if save_best_only = TRUE, it only saves when the model
is considered the "best" and the latest best model according to the
quantity monitored will not be overwritten. If filepath doesn't
contain formatting options like {epoch} then filepath will be
overwritten by each new better model.
if TRUE, then only the model's weights will be saved
(model |> save_model_weights(filepath)), else the full model is saved
(model |> save_model(filepath)).
one of {"auto", "min", "max"}. If save_best_only = 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. In "auto" mode, the mode is set to "max" if the
quantities monitored are "acc" or start with "fmeasure" and are
set to "min" for the rest of the quantities.
"epoch" or integer. When using "epoch", the callback
saves the model after each epoch. When using integer, the callback
saves the model at end of this many batches. If the Model is
compiled with steps_per_execution = N, then the saving criteria will
be checked every Nth batch. Note that if the saving isn't aligned to
epochs, the monitored metric may potentially be less reliable (it
could reflect as little as 1 batch, since the metrics get reset
every epoch). Defaults to "epoch".
Floating point initial "best" value of the
metric to be monitored. Only applies if save_best_value = TRUE. Only
overwrites the model weights already saved if the performance of
current model is better than this value.
model <- keras_model_sequential(input_shape = c(10)) |>
layer_dense(1, activation = "sigmoid") |>
compile(loss = "binary_crossentropy", optimizer = "adam",
metrics = c('accuracy'))EPOCHS <- 10
checkpoint_filepath <- tempfile('checkpoint-model-', fileext = ".keras")
model_checkpoint_callback <- callback_model_checkpoint(
filepath = checkpoint_filepath,
monitor = 'val_accuracy',
mode = 'max',
save_best_only = TRUE
)
# Model is saved at the end of every epoch, if it's the best seen so far.
model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),
epochs = EPOCHS, validation_split = .5, verbose = 0,
callbacks = list(model_checkpoint_callback))
# The model (that are considered the best) can be loaded as -
load_model(checkpoint_filepath)
## Model: "sequential"
## +---------------------------------+------------------------+---------------+
## | Layer (type) | Output Shape | Param # |
## +=================================+========================+===============+
## | dense (Dense) | (None, 1) | 11 |
## +---------------------------------+------------------------+---------------+
## Total params: 35 (144.00 B)
## Trainable params: 11 (44.00 B)
## Non-trainable params: 0 (0.00 B)
## Optimizer params: 24 (100.00 B)# Alternatively, one could checkpoint just the model weights as -
checkpoint_filepath <- tempfile('checkpoint-', fileext = ".weights.h5")
model_checkpoint_callback <- callback_model_checkpoint(
filepath = checkpoint_filepath,
save_weights_only = TRUE,
monitor = 'val_accuracy',
mode = 'max',
save_best_only = TRUE
)# Model weights are saved at the end of every epoch, if it's the best seen
# so far.
# same as above
model |> fit(x = random_uniform(c(2, 10)), y = op_ones(2, 1),
epochs = EPOCHS, validation_split = .5, verbose = 0,
callbacks = list(model_checkpoint_callback))
# The model weights (that are considered the best) can be loaded
model |> load_model_weights(checkpoint_filepath)
Other callbacks:
Callback()
callback_backup_and_restore()
callback_csv_logger()
callback_early_stopping()
callback_lambda()
callback_learning_rate_scheduler()
callback_reduce_lr_on_plateau()
callback_remote_monitor()
callback_swap_ema_weights()
callback_tensorboard()
callback_terminate_on_nan()