From: François Fleuret Date: Tue, 4 Jul 2023 15:49:20 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=f29d0fa816414f74efed3b9ccdad56fdbd346298;p=culture.git Update. --- diff --git a/expr.py b/expr.py index b453f23..d3883d5 100755 --- a/expr.py +++ b/expr.py @@ -48,7 +48,7 @@ def generate_program(nb_variables, length): variables = set() while len(s) < length: v = random_var(nb_variables=nb_variables) - s += v + "=" + random_expr(variables, budget=min(20, length - 3 - len(s))) + ";" + s += v + "=" + random_expr(variables, budget=20) + ";" variables.add(v) return s, variables diff --git a/main.py b/main.py index 324aeba..beafc19 100755 --- a/main.py +++ b/main.py @@ -120,6 +120,13 @@ parser.add_argument("--stack_nb_digits", type=int, default=3) parser.add_argument("--stack_fraction_values_for_train", type=float, default=None) +############################## +# Expr options + +parser.add_argument("--expr_nb_variables", type=int, default=5) + +parser.add_argument("--expr_sequence_length", type=int, default=30) + ###################################################################### args = parser.parse_args() @@ -1012,18 +1019,26 @@ class TaskExpr(Task): self, nb_train_samples, nb_test_samples, + nb_variables, + sequence_length, batch_size, device=torch.device("cpu"), ): self.batch_size = batch_size self.device = device - train_sequences = expr.generate_sequences(nb_train_samples) - test_sequences = expr.generate_sequences(nb_test_samples) + train_sequences = expr.generate_sequences( + nb_train_samples, nb_variables=nb_variables, length=sequence_length + ) + test_sequences = expr.generate_sequences( + nb_test_samples, nb_variables=nb_variables, length=sequence_length + ) self.char2id = dict( [ (c, n) - for n, c in enumerate(set("#"+"".join(train_sequences + test_sequences))) + for n, c in enumerate( + set("#" + "".join(train_sequences + test_sequences)) + ) ] ) self.id2char = dict([(n, c) for c, n in self.char2id.items()]) @@ -1074,9 +1089,9 @@ class TaskExpr(Task): def compute_nb_correct(input): result = input.clone() - space = self.char2id["#"] + filler, space = self.char2id["#"], self.char2id[" "] ar_mask = (result == space).long().cumsum(dim=1).clamp(max=1) - result = (1 - ar_mask) * result + space * ar_mask + result = (1 - ar_mask) * result + filler * ar_mask masked_inplace_autoregression( model, self.batch_size, result, ar_mask, device=self.device ) @@ -1096,9 +1111,9 @@ class TaskExpr(Task): # Log a few generated sequences input = self.test_input[:10] result = input.clone() - space = self.char2id["#"] + filler, space = self.char2id["#"], self.char2id[" "] ar_mask = (result == space).long().cumsum(dim=1).clamp(max=1) - result = (1 - ar_mask) * result + space * ar_mask + result = (1 - ar_mask) * result + filler * ar_mask for n in range(result.size(0)): s = "".join([self.id2char[k.item()] for k in result[n]]) log_string(f"test_before {s}") @@ -1193,6 +1208,8 @@ elif args.task == "expr": task = TaskExpr( nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, + nb_variables=args.expr_nb_variables, + sequence_length=args.expr_sequence_length, batch_size=args.batch_size, device=device, )