class Task:
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
pass
def vocabulary_size(self):
self.train_input = self.tensorize(self.train_descr)
self.test_input = self.tensorize(self.test_descr)
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
def compute_error(
self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
):
+ model_device = next(model.parameters()).device
nb_total, nb_correct = 0, 0
count = torch.zeros(
self.width * self.height,
self.width * self.height,
- device=self.device,
+ device=model_device,
dtype=torch.int64,
)
for input in self.batches(split, nb_to_use):
+ input = input.to(model_device)
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
eol = " " if j < count.size(1) - 1 else "\n"
f.write(f"{count[i,j]}{eol}")
- input = self.test_input[:48]
+ input = self.test_input[:48].to(next(model.parameters()).device)
result = input.clone()
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
device=self.device,
)
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ for label, input in [
+ ("train", self.train_input[:32]),
+ ("test", self.test_input[:32]),
+ ]:
+ output = model(BracketedSequence(input)).x
+ output = output.log_softmax(dim=-1)
+ filename = os.path.join(
+ result_dir, f"stack_with_crossentropy_{n_epoch:04d}_{label}.txt"
+ )
+ with open(filename, "w") as f:
+ for n in range(input.size(0)):
+ s = stack.seq_to_str(
+ input[n], nb_stacks=self.nb_stacks, nb_digits=self.nb_digits
+ )
+ for t, k, w in zip(range(input[n].size(0)), input[n], s.split(" ")):
+ u = (
+ " " * (10 - len(w))
+ + w
+ + " "
+ + str(output[n][t][k].exp().item())
+ + "\n"
+ )
+ f.write(u)
+ f.write("\n")
+ logger(f"wrote {filename}")
+ #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
for n in range(result.size(0)):
logger(
f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
self.t_nul = self.token2id["#"]
self.t_true = self.token2id["true"]
self.t_false = self.token2id["false"]
- self.t_pipe = self.token2id["|"]
+ # self.t_pipe = self.token2id["|"]
# Tokenize the train and test sets
self.train_input = self.str2tensor(self.train_descr)
None if len(self.play_descr) == 0 else self.str2tensor(self.play_descr)
)
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
for batch in tqdm.tqdm(
progress_bar_desc=None,
)
warnings.warn("keeping thinking snapshots", RuntimeWarning)
- snapshots.append(result[:10].detach().clone())
+ snapshots.append(result[:100].detach().clone())
# Generate iteration after iteration
# Set the lookahead_reward to UNKNOWN for the next iterations
result[
:, u + self.world.index_lookahead_reward
- ] = self.world.lookahead_reward2code(gree.REWARD_UNKNOWN)
+ ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
with open(filename, "w") as f:
- for n in range(10):
+ for n in range(snapshots[0].size(0)):
for s in snapshots:
lr, s, a, r = self.world.seq2episodes(
s[n : n + 1],