########################################
-parser.add_argument("--nb_epochs", type=int, default=None)
+parser.add_argument("--nb_epochs", type=int, default=25)
parser.add_argument("--batch_size", type=int, default=None)
parser.add_argument("--expr_input_file", type=str, default=None)
-##############################
-# World options
-
-parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
-
######################################################################
args = parser.parse_args()
######################################################################
default_task_args = {
+ "addition": {
+ "model": "352M",
+ "batch_size": 25,
+ "nb_train_samples": 250000,
+ "nb_test_samples": 10000,
+ },
"byheart": {
"model": "37M",
- "nb_epochs": 2,
"batch_size": 25,
"nb_train_samples": 50000,
"nb_test_samples": 10000,
},
- "learnop": {
+ "expr": {
+ "model": "352M",
+ "batch_size": 25,
+ "nb_train_samples": 2500000,
+ "nb_test_samples": 10000,
+ },
+ "grid": {
"model": "37M",
- "nb_epochs": 15,
"batch_size": 25,
- "nb_train_samples": 50000,
+ "nb_train_samples": 250000,
"nb_test_samples": 10000,
},
"guessop": {
"model": "352M",
- "nb_epochs": 5,
"batch_size": 25,
"nb_train_samples": 1000000,
"nb_test_samples": 10000,
},
- "twotargets": {
+ "learnop": {
"model": "37M",
- "nb_epochs": 10,
"batch_size": 25,
"nb_train_samples": 50000,
"nb_test_samples": 10000,
},
- "addition": {
- "model": "352M",
- "nb_epochs": 50,
- "batch_size": 25,
- "nb_train_samples": 250000,
+ "maze": {
+ "model": "37M",
+ "batch_size": 5,
+ "nb_train_samples": 100000,
"nb_test_samples": 10000,
},
"picoclvr": {
"model": "37M",
- "nb_epochs": 25,
"batch_size": 25,
"nb_train_samples": 250000,
"nb_test_samples": 10000,
},
- "mnist": {
- "model": "37M",
- "nb_epochs": 25,
- "batch_size": 10,
- "nb_train_samples": 60000,
- "nb_test_samples": 10000,
- },
- "maze": {
- "model": "37M",
- "nb_epochs": 25,
+ "rpl": {
+ "model": "352M",
"batch_size": 5,
- "nb_train_samples": 100000,
+ "nb_train_samples": 2500000,
"nb_test_samples": 10000,
},
"snake": {
"model": "37M",
- "nb_epochs": 5,
"batch_size": 25,
"nb_train_samples": 250000,
"nb_test_samples": 10000,
},
"stack": {
"model": "37M",
- "nb_epochs": 15,
"batch_size": 25,
"nb_train_samples": 100000,
"nb_test_samples": 1000,
},
- "expr": {
- "model": "352M",
- "nb_epochs": 25,
- "batch_size": 25,
- "nb_train_samples": 2500000,
- "nb_test_samples": 10000,
- },
- "rpl": {
- "model": "122M",
- "nb_epochs": 50,
- "batch_size": 5,
- "nb_train_samples": 1000000,
- "nb_test_samples": 10000,
- },
- "world": {
+ "twotargets": {
"model": "37M",
- "nb_epochs": 10,
"batch_size": 25,
- "nb_train_samples": 25000,
- "nb_test_samples": 1000,
+ "nb_train_samples": 50000,
+ "nb_test_samples": 10000,
},
- "grid": {
+ "mnist": {
"model": "37M",
- "nb_epochs": 25,
- "batch_size": 25,
- "nb_train_samples": 250000,
+ "batch_size": 10,
+ "nb_train_samples": 60000,
"nb_test_samples": 10000,
},
}
device=device,
)
-elif args.task == "world":
- task = tasks.World(
- nb_train_samples=args.nb_train_samples,
- nb_test_samples=args.nb_test_samples,
- batch_size=args.batch_size,
- vqae_nb_epochs=args.world_vqae_nb_epochs,
- logger=log_string,
- device=device,
- )
-
else:
raise ValueError(f"Unknown task {args.task}")