contrib.keras.callbacks.ModelCheckpoint

tf.contrib.keras.callbacks.ModelCheckpoint

class tf.contrib.keras.callbacks.ModelCheckpoint

Defined in tensorflow/contrib/keras/python/keras/callbacks.py.

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)