# tf.keras.callbacks.ModelCheckpoint

## Class ModelCheckpoint

Inherits From: Callback

Save the model after every epoch.

filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end).

For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5, then the model checkpoints will be saved with the epoch number and the validation loss in the filename.

#### Arguments:

• 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.
• mode: 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 direction is automatically inferred from the name of the monitored quantity.
• 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)).
• period: Interval (number of epochs) between checkpoints.

## Methods

### __init__

__init__(
filepath,
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1
)


### on_batch_begin

on_batch_begin(
batch,
logs=None
)


### on_batch_end

on_batch_end(
batch,
logs=None
)


### on_epoch_begin

on_epoch_begin(
epoch,
logs=None
)


### on_epoch_end

on_epoch_end(
epoch,
logs=None
)


### on_train_begin

on_train_begin(logs=None)


### on_train_end

on_train_end(logs=None)


### set_model

set_model(model)


### set_params

set_params(params)