Can someone please post a straightforward example of Keras using a callback to save a model after every epoch? I can find examples of saving weights, but I want to be able to save a completely functioning model after every training epoch.


Setting 'save_weights_only' to False in the Keras callback 'ModelCheckpoint' will save the full model; this example taken from the link above will save a full model every epoch, regardless of performance:

keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1)

Some more examples are found here, including saving only improved models and loading the saved models.


Make sure to include epoch variable in your filepath. Otherwise your saved model will be replaced after every epoch.

filepath = "saved-model-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=False, mode='max')

For more examples, check here.

  • 2
    $\begingroup$ Welcome to the site! And thanks, I appreciate that addition to the answer $\endgroup$ – I_Play_With_Data Feb 28 '19 at 13:01

I wrote my own ModelCheckpoint class as I have to call a special save_pretrained method:

class ModelCheckpoint(Callback):

    def __init__(self, freq, directory):
        self.freq = freq
        self.directory = directory

    def on_epoch_begin(self, epoch, logs=None):
        if self.freq > 0 and epoch % self.freq == 0:
            self.model.save_pretrained(Path(directory, str(epoch)))

    def on_train_end(self, logs=None):

It always saves the model every freq epochs and at the end of the training.


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