self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
# A bit of paranoia never hurts
assert (
self.nb_codes <= max_nb_codes
and self.train_input.min() >= 0
and self.test_input.min() >= 0
- and tuple(self.train_ar_mask.unique()) == (0, 1)
- and tuple(self.test_ar_mask.unique()) == (0, 1)
+ and tuple(x.item() for x in self.train_ar_mask.unique()) in { (0,), (1,), (0,1) }
+ and tuple(x.item() for x in self.test_ar_mask.unique()) in { (0,), (1,), (0,1) }
)
def batches(self, split="train", nb_to_use=-1, desc=None):
f" {n_epoch} ground truth {self.problem.seq2str(st)}"
)
- nb_total = ar_mask.sum().item()
- nb_correct = ((result == input).long() * ar_mask).sum().item()
+ nb_total, nb_correct = self.problem.compute_nb_correct(input, ar_mask, result)
+
+ # nb_total = ar_mask.sum().item()
+ # nb_correct = ((result == input).long() * ar_mask).sum().item()
return nb_total, nb_correct
self.train_input = seq[:nb_train_samples]
self.train_q_test_set = q_test_set[:nb_train_samples]
+ self.train_ref_test_errors = test_error[:nb_train_samples]
self.test_input = seq[nb_train_samples:]
self.test_q_test_set = q_test_set[nb_train_samples:]
- self.ref_test_errors = test_error
+ self.test_ref_test_errors = test_error[nb_train_samples:]
+
+ filename = os.path.join(result_dir, f"train_errors_ref.dat")
+ with open(filename, "w") as f:
+ for e in self.train_ref_test_errors:
+ f.write(f"{e}\n")
filename = os.path.join(result_dir, f"test_errors_ref.dat")
with open(filename, "w") as f:
- for e in self.ref_test_errors:
+ for e in self.test_ref_test_errors:
f.write(f"{e}\n")
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1