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This repository has been archived by the owner on Nov 17, 2023. It is now read-only.
In file example/image-classification/common/fit.py at line 295, the mx.callback.Speedometer is initialized with auto_reset unset, which will be its default value, i.e. True. This will make the logged epoch accuracy incorrect, since the module and the speedometer share the same metric.
Note that when auto_reset=True, the batch-wise accuracy is correct, but epoch-wise is wrong. When auto_reset=False, the batch-wise one is incorrect, but epoch-wise is correct.
I believe there are two fixes:
Manually set auto_reset=False to log accumulated accuracy for each batch.
Set an independent metric for the speedometer.
The text was updated successfully, but these errors were encountered:
@hxhxhx88 Upon further investigation, it was found that this observation is expected behavior.
"INFO:root:Epoch[1] Train-accuracy=" is not the epoch accuracy - the log is misleading (Ref: #10437). The plan is to remove this print statement altogether.
The log for batch is based on a user-specified value which prints a log at regular intervals (--disp-batches in fit.py).
A separate metric is being maintained for epoch metric vs batch-wise metric. @hxhxhx88 Please have a look at PR #12182. It would be great if you could verify it at your end as well.
In file example/image-classification/common/fit.py at line 295, the
mx.callback.Speedometer
is initialized withauto_reset
unset, which will be its default value, i.e.True
. This will make the logged epoch accuracy incorrect, since the module and the speedometer share the same metric.Following is the current log for 1 epoch:
Following is the log with
auto_reset
set toFalse
:Note that when
auto_reset=True
, the batch-wise accuracy is correct, but epoch-wise is wrong. Whenauto_reset=False
, the batch-wise one is incorrect, but epoch-wise is correct.I believe there are two fixes:
auto_reset=False
to log accumulated accuracy for each batch.The text was updated successfully, but these errors were encountered: