nb_test_samples, height=self.height, width=self.width
).to(device)
+ # print()
+ # for a in world.seq2str(self.train_input):
+ # print(a)
+ # for a in world.seq2str(self.test_input):
+ # print(a)
+ # exit(0)
+
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
self.train_quizzes = []
# Check how many of the other models can solve them in both
# directions
- nb_correct = 0
+ nb_correct = []
for m in other_models:
result = quizzes.clone()
(reverse_quizzes == reverse_result).long().min(dim=-1).values
)
- nb_correct += correct * reverse_correct
+ nb_correct.append((correct * reverse_correct)[None, :])
+
+ nb_correct = torch.cat(nb_correct, dim=0)
+
+ filename = os.path.join(result_dir, "correct_{n_epoch:04d}.dat")
+ with open(filename, "w") as f:
+ for k in nb_correct:
+ f.write(f"{k}\n")
- return quizzes, nb_correct
+ return quizzes, nb_correct.sum(dim=0)