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Save the model after every epoch.

Inherits From: Callback

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.

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. If filepath doesn't contain formatting options like {epoch} then filepath will be overwritten by each new better model.
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 (
save_freq '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 a batch at which this many samples have been seen since last saving. 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'
**kwargs Additional arguments for backwards compatibility. Possible key is period.



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