Comments (5)
hi, @jiaruHithub thanks for your interest. The hyperparameters used in transfer learning are exactly the default parameters in the file, pretraining.py
and finetune.py
in this directory . Or you can just use the checkpoint we provieded.
from graphmae.
hi, @jiaruHithub thanks for your interest. The hyperparameters used in transfer learning are exactly the default parameters in the file,
pretraining.py
andfinetune.py
in this directory . Or you can just use the checkpoint we provieded.
Thanks, now I use the pretrianed model you provided, but there is many transfer dataset in paper, all the hyper-parameters for different tranfer dataset is same ? Like the all hyper-parameters in finetune.py
for transfer to bbbp
and bace
is same ?
from graphmae.
hi, @jiaruHithub thanks for your interest. The hyperparameters used in transfer learning are exactly the default parameters in the file,
pretraining.py
andfinetune.py
in this directory . Or you can just use the checkpoint we provieded.
Thanks, now I use the pretrianed model you provided, but there is many transfer dataset in paper, all the hyper-parameters for different tranfer dataset is same ? Like the all hyper-parameters in finetune.py
for transfer to bbbp
and bace
is same ?
from graphmae.
These datasets share most hyper-parameters. Specially, we use learning_rate decay
for bace
, and early-stopping
for muv/hiv
. The two operations have been implemented in our code. And all other hyper-parameters are the same across all datasets. And we will update this soon.
from graphmae.
Thanks a lot !
from graphmae.
Related Issues (20)
- reproduction issue HOT 1
- Find implement issue in PyG version HOT 1
- Great work! Have you tried heterogeneous graphs? HOT 1
- link prediction HOT 1
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- how to set hyperparameters(e.g. num_hidden, num_heads) HOT 5
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- closed
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