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Update.
author
François Fleuret
<francois@fleuret.org>
Wed, 5 Jul 2023 07:25:40 +0000
(09:25 +0200)
committer
François Fleuret
<francois@fleuret.org>
Wed, 5 Jul 2023 07:25:40 +0000
(09:25 +0200)
main.py
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diff --git
a/main.py
b/main.py
index
15e6d99
..
32447bf
100755
(executable)
--- a/
main.py
+++ b/
main.py
@@
-1119,33
+1119,40
@@
class TaskExpr(Task):
#######################################################################
# Comput predicted vs. true variable values
#######################################################################
# Comput predicted vs. true variable values
+ nb_delta = torch.zeros(5, dtype=torch.int64)
+ nb_missed = 0
+
values_input = expr.extract_results([self.seq2str(s) for s in input])
values_input = expr.extract_results([self.seq2str(s) for s in input])
- max_input = max([max(x.values()) for x in values_input])
values_result = expr.extract_results([self.seq2str(s) for s in result])
values_result = expr.extract_results([self.seq2str(s) for s in result])
- max_result = max(
- [-1 if len(x) == 0 else max(x.values()) for x in values_result]
- )
-
- nb_missing = torch.zeros(max_input + 1)
- nb_predicted = torch.zeros(max_input + 1, max_result + 1)
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:
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_miss
ing[vi]
+= 1
+ nb_miss
ed
+= 1
else:
else:
- nb_predicted[vi, vr] += 1
+ d = abs(vr-vi)
+ if d >= nb_delta.size(0):
+ nb_missed += 1
+ else:
+ nb_delta[d] += 1
+
######################################################################
######################################################################
- return nb_total, nb_correct
+ return nb_total, nb_correct
, nb_delta, nb_missed
- test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+ 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}%"
)
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]
##############################################################
# Log a few generated sequences
input = self.test_input[:10]