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):
_, _, 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])
- logger(f"GROUND-TRUTH PROG [{gt_prog}] PREDICTED PROG [{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 = " CORRECT" if correct else ""
+ 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" [{start_stack}] -> [{result_stack}] TARGET [{target_stack}]{comment}"
+ f" {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]"
)
nb_to_log -= 1