From 3f09462033feac19ad72ac1a4b8690e6330df22d Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Wed, 26 Jul 2023 10:31:47 -1000 Subject: [PATCH] Update. --- main.py | 92 ++++++++++++++++++++++++++++++++++++--------------------- 1 file changed, 58 insertions(+), 34 deletions(-) diff --git a/main.py b/main.py index 9a3d346..303abc1 100755 --- a/main.py +++ b/main.py @@ -122,9 +122,9 @@ parser.add_argument("--maze_nb_walls", type=int, default=45) ############################## # Snake options -parser.add_argument("--snake_height", type=int, default=6) +parser.add_argument("--snake_height", type=int, default=9) -parser.add_argument("--snake_width", type=int, default=8) +parser.add_argument("--snake_width", type=int, default=12) parser.add_argument("--snake_nb_colors", type=int, default=5) @@ -171,10 +171,34 @@ if args.result_dir is None: ###################################################################### default_task_args = { - "sandbox": { - "nb_epochs": 50, + "byheart": { + "nb_epochs": 5, "batch_size": 25, - "nb_train_samples": 100000, + "nb_train_samples": 50000, + "nb_test_samples": 10000, + }, + "learnop": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 50000, + "nb_test_samples": 10000, + }, + "guessop": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 50000, + "nb_test_samples": 10000, + }, + "twotargets": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 50000, + "nb_test_samples": 10000, + }, + "addition": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 50000, "nb_test_samples": 10000, }, "picoclvr": { @@ -186,7 +210,7 @@ default_task_args = { "mnist": { "nb_epochs": 25, "batch_size": 10, - "nb_train_samples": 250000, + "nb_train_samples": 60000, "nb_test_samples": 10000, }, "maze": { @@ -198,7 +222,7 @@ default_task_args = { "snake": { "nb_epochs": 5, "batch_size": 25, - "nb_train_samples": 250000, + "nb_train_samples": 50000, "nb_test_samples": 10000, }, "stack": { @@ -339,7 +363,7 @@ if args.task == "byheart": logger=log_string, device=device, ) - + args.max_percents_of_test_in_train = -1 elif args.task == "learnop": task = tasks.SandBox( @@ -563,33 +587,33 @@ train_set_perplexity = math.exp(entropy) ###################################################################### # A bit of paranoia never hurts +if args.max_percents_of_test_in_train >= 0: + + def subsets_as_tuples(batches, cs): + s = set() + for batch in batches: + for x in batch: + s.add(tuple([v.item() for v in x])) + if len(s) == cs: + yield s + s = set() + yield s + + nb_test, nb_in_train = 0, 0 + for test_subset in subsets_as_tuples(task.batches(split="test"), 25000): + in_train = set() + for train_subset in subsets_as_tuples(task.batches(split="train"), 25000): + in_train.update(test_subset.intersection(train_subset)) + nb_in_train += len(in_train) + nb_test += len(test_subset) + + log_string( + f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set" + ) -def subsets_as_tuples(batches, cs): - s = set() - for batch in batches: - for x in batch: - s.add(tuple([v.item() for v in x])) - if len(s) == cs: - yield s - s = set() - yield s - - -nb_test, nb_in_train = 0, 0 -for test_subset in subsets_as_tuples(task.batches(split="test"), 25000): - in_train = set() - for train_subset in subsets_as_tuples(task.batches(split="train"), 25000): - in_train.update(test_subset.intersection(train_subset)) - nb_in_train += len(in_train) - nb_test += len(test_subset) - -log_string( - f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set" -) - -assert ( - nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100 -), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set" + assert ( + nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100 + ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set" ############################## -- 2.39.5