X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=9dee679fbf1bdcda0faac54cc77179072c4ad0a4;hb=ca5b98d1517b8ce2367887bbad2205f27d55e0b3;hp=35bf02c0123815b31d479df63f935839c7523b33;hpb=02b0a7bb770f07f2e91f1c77b899815516087b6a;p=picoclvr.git

diff --git a/main.py b/main.py
index 35bf02c..9dee679 100755
--- a/main.py
+++ b/main.py
@@ -1030,8 +1030,9 @@ class TaskExpr(Task):
         train_sequences = expr.generate_sequences(
             nb_train_samples,
             nb_variables=nb_variables,
-            length=2 * sequence_length,
-            randomize_length=True,
+            length=sequence_length,
+            # length=2 * sequence_length,
+            # randomize_length=True,
         )
         test_sequences = expr.generate_sequences(
             nb_test_samples,
@@ -1115,14 +1116,51 @@ class TaskExpr(Task):
                 nb_total = input.size(0)
                 nb_correct = (input == result).long().min(1).values.sum()
 
-                return nb_total, nb_correct
+                #######################################################################
+                # Comput predicted vs. true variable values
 
-            test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+                nb_delta = torch.zeros(5, dtype=torch.int64)
+                nb_missed = 0
+
+                values_input = expr.extract_results([self.seq2str(s) for s in input])
+                values_result = expr.extract_results([self.seq2str(s) for s in result])
+
+                for i, r in zip(values_input, values_result):
+                    for n, vi in i.items():
+                        vr = r.get(n)
+                        if vr is None or vr < 0:
+                            nb_missed += 1
+                        else:
+                            d = abs(vr - vi)
+                            if d >= nb_delta.size(0):
+                                nb_missed += 1
+                            else:
+                                nb_delta[d] += 1
+
+                ######################################################################
+
+                return nb_total, nb_correct, nb_delta, nb_missed
+
+            (
+                test_nb_total,
+                test_nb_correct,
+                test_nb_delta,
+                test_nb_missed,
+            ) = compute_nb_correct(self.test_input[:1000])
 
             log_string(
                 f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
             )
 
+            nb_total = test_nb_delta.sum() + test_nb_missed
+            for d in range(test_nb_delta.size(0)):
+                log_string(
+                    f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
+                )
+            log_string(
+                f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
+            )
+
             ##############################################################
             # Log a few generated sequences
             input = self.test_input[:10]