Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index 8b5d9a4..43241dd 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -249,8 +249,10 @@ if args.problem == "sky":
         nb_iterations=args.sky_nb_iterations,
         speed=args.sky_speed,
     )
+    back_accuracy = False
 elif args.problem == "reasoning":
-    problem = reasoning.Reasoning()
+    problem = reasoning.Reasoning(device=device)
+    back_accuracy = True
 else:
     raise ValueError
 
@@ -258,6 +260,7 @@ quizz_machine = quizz_machine.QuizzMachine(
     problem=problem,
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
+    back_accuracy=back_accuracy,
     batch_size=args.physical_batch_size,
     result_dir=args.result_dir,
     logger=log_string,
@@ -508,6 +511,9 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 for n_epoch in range(args.nb_epochs):
     log_string(f"--- epoch {n_epoch} ----------------------------------------")
 
+    cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
+    log_string(f"current_test_accuracies {cta}")
+
     # Select, improve, and eval the worst model
 
     weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
@@ -528,9 +534,6 @@ for n_epoch in range(args.nb_epochs):
         f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
     )
 
-    cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
-    log_string(f"current_test_accuracies {cta}")
-
     # Replace a fraction of the w_quizzes with fresh ones
 
     quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)