From 719785dbea77989a54bf7592bb6919f2e8f3f6c5 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Sat, 6 Jul 2024 16:47:57 +0300 Subject: [PATCH] Update. --- main.py | 33 ++++++++++++++++++++------------- quiz_machine.py | 4 ++-- 2 files changed, 22 insertions(+), 15 deletions(-) diff --git a/main.py b/main.py index 6b46fa0..a2a771f 100755 --- a/main.py +++ b/main.py @@ -19,11 +19,6 @@ import sky, grids, quiz_machine ###################################################################### -nb_new_c_quizzes_for_train = 1000 -nb_new_c_quizzes_for_test = 100 - -###################################################################### - if torch.cuda.is_available(): device = torch.device("cuda") torch.backends.cuda.matmul.allow_tf32 = True @@ -124,14 +119,6 @@ if args.result_dir is None: ###################################################################### -if args.dirty_debug: - args.accuracy_to_make_c_quizzes = 0.0 - args.nb_gpts = 2 - nb_new_c_quizzes_for_train = 100 - nb_new_c_quizzes_for_test = 10 - -###################################################################### - default_args = { "model": "37M", "batch_size": 100, @@ -521,12 +508,30 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") ###################################################################### +nb_new_c_quizzes_for_train = args.nb_train_samples // 50 +nb_new_c_quizzes_for_test = args.nb_test_samples // 50 + +log_string( + f"nb_new_c_quizzes_for_train {nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {nb_new_c_quizzes_for_test}" +) + +###################################################################### + +if args.dirty_debug: + args.accuracy_to_make_c_quizzes = 0.0 + args.nb_gpts = 2 + nb_new_c_quizzes_for_train = 100 + nb_new_c_quizzes_for_test = 10 + +###################################################################### + 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)) @@ -547,10 +552,12 @@ for n_epoch in range(args.nb_epochs): f"test_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}" ) + ################################################## # Replace a fraction of the w_quizzes with fresh ones quiz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts) + ################################################## # If all the models are good enough, generate new quizzes and # re-compute the test errors diff --git a/quiz_machine.py b/quiz_machine.py index cb187be..45b2247 100755 --- a/quiz_machine.py +++ b/quiz_machine.py @@ -360,11 +360,11 @@ class QuizMachine: backward_nb_total = correct[n_backward].size(0) self.logger( - f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total}" + f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)" ) self.logger( - f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total}" + f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)" ) return result, correct -- 2.39.5