The current implementation of curriculum learning forces by default that it needs to be finished even if convergence has been reached (in terms of loss < threshold):
# If the 'must_finish' key is not present in config then then it will be finished by default self.params['training']['curriculum_learning'].add_default_params({'must_finish': True})
While I think this is okay, it'd be great if the trainer would indicate that. For instance, if the loss threshold for a run of MAES on SerialRecall is set to 1e-2, MAES will most likely converge before the end of curriculem learning. In this case, a warning message would be great, in the lines of:
if not self.curric_done and converged:self.logger.warning('The model has converged but curriculum has been set with must_finish=True.')
The current implementation of curriculum learning forces by default that it needs to be finished even if convergence has been reached (in terms of loss < threshold):
# If the 'must_finish' key is not present in config then then it will be finished by default self.params['training']['curriculum_learning'].add_default_params({'must_finish': True})While I think this is okay, it'd be great if the trainer would indicate that. For instance, if the loss threshold for a run of MAES on SerialRecall is set to 1e-2, MAES will most likely converge before the end of curriculem learning. In this case, a warning message would be great, in the lines of:
if not self.curric_done and converged:self.logger.warning('The model has converged but curriculum has been set with must_finish=True.')