"--task",
type=str,
default="twotargets",
- help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp",
+ help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp, greed",
)
parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
parser.add_argument("--mixing_deterministic_start", action="store_true", default=False)
+##############################
+# greed options
+
+parser.add_argument("--greed_height", type=int, default=5)
+
+parser.add_argument("--greed_width", type=int, default=7)
+
+parser.add_argument("--greed_T", type=int, default=25)
+
+parser.add_argument("--greed_nb_walls", type=int, default=5)
+
+parser.add_argument("--greed_nb_coins", type=int, default=2)
+
######################################################################
args = parser.parse_args()
"nb_train_samples": 60000,
"nb_test_samples": 10000,
},
+ "greed": {
+ "model": "37M",
+ "batch_size": 25,
+ "nb_train_samples": 25000,
+ "nb_test_samples": 10000,
+ },
}
if args.task in default_task_args:
nb_train_samples=args.nb_train_samples,
nb_test_samples=args.nb_test_samples,
batch_size=args.batch_size,
+ shuffle=True,
device=device,
)
args.max_percents_of_test_in_train = 0
device=device,
)
+elif args.task == "greed":
+ task = tasks.Greed(
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ batch_size=args.batch_size,
+ height=args.greed_height,
+ width=args.greed_width,
+ T=args.greed_T,
+ nb_walls=args.greed_nb_walls,
+ nb_coins=args.greed_nb_coins,
+ logger=log_string,
+ device=device,
+ )
+
else:
raise ValueError(f"Unknown task {args.task}")
######################################################################
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
-
# Compute the entropy of the training tokens
token_count = 0
-for input in task.batches(split="train"):
+for input in task.batches(split="train", desc="train-entropy"):
token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
token_probas = token_count / token_count.sum()
entropy = -torch.xlogy(token_probas, token_probas).sum()
yield s
nb_test, nb_in_train = 0, 0
- for test_subset in subsets_as_tuples(task.batches(split="test"), 25000):
+ for test_subset in subsets_as_tuples(
+ task.batches(split="test", desc="test-check"), 25000
+ ):
in_train = set()
- for train_subset in subsets_as_tuples(task.batches(split="train"), 25000):
+ for train_subset in subsets_as_tuples(
+ task.batches(split="train", desc="train-check"), 25000
+ ):
in_train.update(test_subset.intersection(train_subset))
nb_in_train += len(in_train)
nb_test += len(test_subset)
nb_samples_seen = 0
-if nb_epochs_finished >= nb_epochs:
+if nb_epochs_finished >= args.nb_epochs:
task.produce_results(
n_epoch=nb_epochs_finished,
model=model,
time_pred_result = None
-for n_epoch in range(nb_epochs_finished, nb_epochs):
+for n_epoch in range(nb_epochs_finished, args.nb_epochs):
learning_rate = learning_rate_schedule[n_epoch]
log_string(f"learning_rate {learning_rate}")