X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=main.py;h=d63398c1246158e4e8b7519366a7a4bd95ccd08d;hb=d283cd3d46a6323fec4c6a0970ac71e553e4a486;hp=10c7b4991218a8d540fed20d6fd12169d71c9484;hpb=c32e471f8153ce4bdf19fb440f1e642eda4b972a;p=culture.git

diff --git a/main.py b/main.py
index 10c7b49..d63398c 100755
--- a/main.py
+++ b/main.py
@@ -79,7 +79,7 @@ parser.add_argument("--dropout", type=float, default=0.1)
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
-parser.add_argument("--reverse_cleanup", action="store_true", default=False)
+parser.add_argument("--both_directions", action="store_true", default=False)
 
 parser.add_argument("--problem", type=str, default="sky")
 
@@ -362,7 +362,7 @@ def run_tests(model, quizz_machine, deterministic_synthesis):
 
             nb_test_samples += input.size(0)
 
-        main_test_accuracy = quizz_machine.produce_results(
+        model.main_test_accuracy = quizz_machine.produce_results(
             n_epoch=n_epoch,
             model=model,
             result_dir=args.result_dir,
@@ -373,8 +373,6 @@ def run_tests(model, quizz_machine, deterministic_synthesis):
 
         log_string(f"test_perplexity {n_epoch} {test_perplexity}")
 
-    model.main_test_accuracy = main_test_accuracy
-
 
 ######################################################################
 
@@ -395,8 +393,6 @@ def create_c_quizzes(
 ):
     recorded = []
 
-    sum_logits, sum_nb_c_quizzes = 0, 0
-
     nb_to_create = nb_for_train + nb_for_test
 
     # ------------------------------------------------------------
@@ -411,13 +407,11 @@ def create_c_quizzes(
         c_quizzes, ave_seq_logproba = quizz_machine.generate_quizzes(
             nb_to_create,
             model_for_generation=model_for_generation,
-            reverse_cleanup=args.reverse_cleanup,
         )
 
-        sum_logits += c_quizzes.size(0) * ave_seq_logproba
-        sum_nb_c_quizzes += c_quizzes.size(0)
-
-        nb_correct = quizz_machine.compute_correctness(c_quizzes, models)
+        nb_correct = quizz_machine.compute_correctness(
+            c_quizzes, models, both_directions=args.both_directions
+        )
 
         if args.dirty_debug:
             nb_correct = torch.randint(
@@ -432,16 +426,19 @@ def create_c_quizzes(
         nb_validated = valid_c_quizzes(recorded, standard_validity).size(0)
 
         log_string(
-            f"keep c_quizzes kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
+            f"keep c_quizzes model {model_for_generation.id} kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
         )
 
-    # ------------------------------------------------------------
+    # store the new c_quizzes which have been validated
 
     new_c_quizzes = valid_c_quizzes(recorded, standard_validity)
 
     quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
     quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
 
+    # save a bunch of images to investigate what quizzes with a
+    # certain nb of correct predictions look like
+
     for n in range(len(models) + 1):
         s = (
             "_validated"
@@ -449,13 +446,12 @@ def create_c_quizzes(
             else ""
         )
 
-        quizz_machine.problem.save_quizzes(
-            valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72],
-            args.result_dir,
-            f"culture_c_quiz_{n_epoch:04d}_N{n}{s}",
-        )
+        q = valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72]
 
-    return sum_logits / sum_nb_c_quizzes
+        if q.size(0) > 0:
+            quizz_machine.save_quizzes(
+                args.result_dir, f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", q
+            )
 
 
 ######################################################################
@@ -488,32 +484,36 @@ 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} ----------------------------------------")
 
+    # Select, improve, and eval the worst model
+
     weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
 
     log_string(
         f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}"
     )
 
-    # improve it
     one_epoch(weakest_model, quizz_machine)
 
     log_string(
         f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
     )
 
-    # test it
     run_tests(weakest_model, quizz_machine, deterministic_synthesis=False)
 
     log_string(
         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):.02f}" for m in models])
+    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 a fresh ones
+    # Replace a fraction of the w_quizzes with fresh ones
+
     quizz_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
+
     if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
         create_c_quizzes(
             models,
@@ -522,7 +522,6 @@ for n_epoch in range(args.nb_epochs):
             nb_for_test=nb_new_c_quizzes_for_test,
         )
 
-        # We update everyone
         for model in models:
             run_tests(model, quizz_machine, deterministic_synthesis=False)