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Here, you are first training on all the batches in the meta-train set, then you are doing validation and testing.
However, the original algorithm seems to record the testing accuracy after training on every batch of tasks in the meta-train set. Do you observe the difference? I know it is a matter of implementation, we could have done the testing simultaneously, but as a matter of keeping consistent with the original implementation and the way others report their accuracy, would it make sense to observe the testing accuracy immediately after training on 1 meta-batch?
Thanks
The text was updated successfully, but these errors were encountered:
Hi Sungyub Kim,
Thanks for the great implementation.
I have a question about your code here:
GBML/main.py
Lines 69 to 71 in 1577e17
Here, you are first training on all the batches in the meta-train set, then you are doing validation and testing.
However, the original algorithm seems to record the testing accuracy after training on every batch of tasks in the meta-train set. Do you observe the difference? I know it is a matter of implementation, we could have done the testing simultaneously, but as a matter of keeping consistent with the original implementation and the way others report their accuracy, would it make sense to observe the testing accuracy immediately after training on 1 meta-batch?
Thanks
The text was updated successfully, but these errors were encountered: