0,
).to(self.device)
+ def seq2str(self, seq):
+ return " ".join([self.id2token[i] for i in seq])
+
def __init__(
self,
nb_train_samples,
nb_test_samples,
batch_size,
+ nb_starting_values=3,
+ max_input=9,
+ prog_len=6,
+ nb_runs=5,
+ logger=None,
device=torch.device("cpu"),
):
super().__init__()
self.device = device
train_sequences = [
- rpl.generate()
+ rpl.generate(
+ nb_starting_values=nb_starting_values,
+ max_input=max_input,
+ prog_len=prog_len,
+ nb_runs=nb_runs,
+ )
for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data")
]
+
test_sequences = [
- rpl.generate() for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data")
+ rpl.generate(
+ nb_starting_values=nb_starting_values,
+ max_input=max_input,
+ prog_len=prog_len,
+ nb_runs=nb_runs,
+ )
+ for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data")
]
symbols = list(
self.train_input = self.tensorize(train_sequences)
self.test_input = self.tensorize(test_sequences)
+ if logger is not None:
+ for x in self.train_input[:10]:
+ end = (x != self.t_nul).nonzero().max().item() + 1
+ seq = [self.id2token[i.item()] for i in x[:end]]
+ s = " ".join(seq)
+ logger(f"example_seq {s}")
+
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
def batches(self, split="train", nb_to_use=-1, desc=None):
device=self.device,
)
- if nb_to_log > 0:
- for x in result[:nb_to_log]:
- s = " ".join([self.id2token[i.item()] for i in x])
- logger(f"check {n_epoch} {s}")
- nb_to_log -= min(nb_to_log, result.size(0))
-
sum_nb_total, sum_nb_errors = 0, 0
- for x in result:
- seq = [self.id2token[i.item()] for i in x]
- nb_total, nb_errors = rpl.check(seq)
- sum_nb_total += nb_total
- sum_nb_errors += nb_errors
+ for x, y in zip(input, result):
+ seq = [self.id2token[i.item()] for i in y]
+ nb_total, nb_errors, prog, stacks = rpl.compute_nb_errors(seq)
+ sum_nb_total += 1
+ sum_nb_errors += 0 if nb_errors == 0 else 1
+ if nb_to_log > 0:
+ gt_seq = [self.id2token[i.item()] for i in x]
+ _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq)
+ gt_prog = " ".join([str(x) for x in gt_prog])
+ prog = " ".join([str(x) for x in prog])
+ comment = "*" if nb_errors == 0 else "-"
+ logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]")
+ for start_stack, target_stack, result_stack, correct in stacks:
+ comment = "*" if correct else "-"
+ start_stack = " ".join([str(x) for x in start_stack])
+ target_stack = " ".join([str(x) for x in target_stack])
+ result_stack = " ".join([str(x) for x in result_stack])
+ logger(
+ f" {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]"
+ )
+ nb_to_log -= 1
return sum_nb_total, sum_nb_errors
- test_nb_total, test_nb_errors = compute_nb_errors(self.test_input, nb_to_log=10)
+ test_nb_total, test_nb_errors = compute_nb_errors(
+ self.test_input[:1000], nb_to_log=10
+ )
logger(
f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"