def __call__(self, model, train, algorithm):
if (model.batches_seen % self.ais_interval) != 0:
return
(train_ll, test_ll, logz) = ais.estimate_likelihood(model,
self.trainset, self.testset, large_ais=False)
self.log(model, train_ll, test_ll, logz)
if model.jobman_channel:
model.jobman_channel.save()
def __call__(self, model, train, algorithm):
if (model.batches_seen % self.ais_interval) != 0:
return
(train_ll, test_ll, logz) = ais.estimate_likelihood(model,
self.trainset,
self.testset,
large_ais=False)
self.log(model, train_ll, test_ll, logz)
if model.jobman_channel:
model.jobman_channel.save()
def __call__(self, model, train, algorithm):
if self.switch_at and (not self.has_switched) and model.epochs >= self.switch_at:
self.switch_to_full_natural(model)
# measure AIS periodically
if (model.epochs % self.ais_interval) == 0:
(train_ll, test_ll, logz) = ais.estimate_likelihood(model,
self.trainset, self.testset, large_ais=False)
if self.switch_threshold and model.epochs > 0 and (not self.has_switched):
improv = train_ll - self.jobman_results['train_ll']
if improv < abs(self.switch_threshold * self.jobman_results['train_ll']):
self.switch_to_full_natural(model)
self.log(model, train_ll, test_ll, logz)
if model.jobman_channel:
model.jobman_channel.save()
