deepctr_torch.callbacks module

class deepctr_torch.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', save_freq='epoch', options=None, **kwargs)

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.

on_epoch_end(epoch, logs=None)

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Args:

epoch: Integer, index of epoch. logs: Dict, metric results for this training epoch, and for the

validation epoch if validation is performed. Validation result keys are prefixed with val_. For training epoch, the values of the

Model’s metrics are returned. Example : `{‘loss’: 0.2, ‘accuracy’:

0.7}`.