X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=main.py;h=ee4e9e5b3aea5fb5f6dcd426bbecbd7687bfbb35;hb=694923fcfa606cf8fc9ee6066ef4bbdea27003ce;hp=45fa68c9eeaf0a0c802d0825ad82c4c1833614e6;hpb=9df1bd18f930f6b4a30b94fed6de684d5ceae3b7;p=culture.git

diff --git a/main.py b/main.py
index 45fa68c..ee4e9e5 100755
--- a/main.py
+++ b/main.py
@@ -183,8 +183,8 @@ for n in vars(args):
 ######################################################################
 
 if args.check:
-    args.nb_train_samples = 500
-    args.nb_test_samples = 100
+    args.nb_train_samples = 25000
+    args.nb_test_samples = 1000
 
 if args.physical_batch_size is None:
     args.physical_batch_size = args.batch_size
@@ -335,23 +335,31 @@ def create_quizzes(
     task,
     nb_for_train=1000,
     nb_for_test=100,
+    desired_average_logits=None,
 ):
     kept = []
 
+    sum_logits = 0
+
     while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
-        new_quizzes, nb_correct = task.create_new_quizzes(
+        nb_to_generate = 4 * (nb_for_train + nb_for_test)
+
+        new_quizzes, nb_correct, _sum_logits = task.create_new_quizzes(
             n_epoch=n_epoch,
             result_dir=args.result_dir,
             logger=log_string,
-            nb=4 * (nb_for_train + nb_for_test),
+            nb=nb_to_generate,
             model=model,
             other_models=other_models,
+            desired_average_logits=desired_average_logits,
         )
 
-        print(nb_correct)
+        sum_logits += _sum_logits
 
         to_keep = new_quizzes[nb_correct == len(other_models) - 1]
-        log_string(f"keep {to_keep.size(0)} quizzes")
+        log_string(
+            f"keep {to_keep.size(0)}/{new_quizzes.size(0)} quizzes ({to_keep.size(0)*100/new_quizzes.size(0):.02f}%)"
+        )
         kept.append(to_keep)
 
     new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
@@ -366,6 +374,8 @@ def create_quizzes(
         log_string,
     )
 
+    return sum_logits / new_quizzes.size(0)
+
 
 ######################################################################
 
@@ -400,11 +410,15 @@ nb_new_quizzes_for_test = 100
 
 if args.check:
     accuracy_to_make_quizzes = 0.0
-    nb_new_quizzes_for_train = 10
+    nb_new_quizzes_for_train = 100
     nb_new_quizzes_for_test = 10
 
+desired_average_logits = None
+
 for n_epoch in range(args.nb_epochs):
-    a = [(model.id, model.main_test_accuracy) for model in models]
+    log_string(f"--- epoch {n_epoch} ----------------------------------------")
+
+    a = [(model.id, float(model.main_test_accuracy)) for model in models]
     a.sort(key=lambda p: p[0])
     log_string(f"current accuracies {a}")
 
@@ -419,6 +433,8 @@ for n_epoch in range(args.nb_epochs):
     # improve it
     one_epoch(model, task)
 
+    task.renew_samples(args.nb_train_samples // args.nb_gpts)
+
     log_string(
         f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
     )
@@ -426,18 +442,31 @@ for n_epoch in range(args.nb_epochs):
     # test it
     run_tests(model, task, deterministic_synthesis=False)
 
-    if model.main_test_accuracy >= accuracy_to_make_quizzes:
+    log_string(
+        f"test_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+    )
+
+    if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_quizzes:
         other_models = models.copy()
         other_models.remove(model)
 
-        create_quizzes(
+        average_logits = create_quizzes(
             model,
             other_models,
             task,
             nb_for_train=nb_new_quizzes_for_train,
             nb_for_test=nb_new_quizzes_for_test,
+            desired_average_logits=desired_average_logits,
         )
 
+        # We keep the first average logits as a reference
+        if desired_average_logits is None:
+            desired_average_logits = average_logits
+        else:
+            log_string(
+                f"desired_average_logits {desired_average_logits} average_logits {average_logits}"
+            )
+
         # We update everyone
         for model in models:
             run_tests(model, task, deterministic_synthesis=False)