parser.add_argument("--greed_nb_walls", type=int, default=5)
+parser.add_argument("--greed_nb_coins", type=int, default=2)
+
######################################################################
args = parser.parse_args()
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,
)
######################################################################
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
-
# Compute the entropy of the training tokens
token_count = 0
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}")