Snapshot Ensembles - Keras

snapshot-ensembles.py

    def get_callbacks(self, model_prefix='Model'):
        """
        Creates a list of callbacks that can be used during training to create a
        snapshot ensemble of the model.
        Args:
            model_prefix: prefix for the filename of the weights.
        Returns: list of 3 callbacks [ModelCheckpoint, LearningRateScheduler,
                 SnapshotModelCheckpoint] which can be provided to the 'fit' function
        """
        if not os.path.exists('weights/'):
            os.makedirs('weights/')

        callback_list = [ModelCheckpoint('weights/%s-Best.h5' % model_prefix, monitor='val_acc',
                                         save_best_only=True, save_weights_only=True),
                         LearningRateScheduler(schedule=self._cosine_anneal_schedule),
                         SnapshotModelCheckpoint(self.T, self.M, fn_prefix='weights/%s' % model_prefix)]

        return callback_list

    def _cosine_anneal_schedule(self, t):
        cos_inner = np.pi * (t % (self.T // self.M))
        cos_inner /= self.T // self.M
        cos_out = np.cos(cos_inner) + 1
        return float(self.alpha_zero / 2 * cos_out)