Make GraphNetworkLayer subclass from tf.Module#337
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boomanaiden154 merged 4 commits intoMay 3, 2025
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Created using spr 1.3.4 [skip ci]
Created using spr 1.3.4
Created using spr 1.3.4 [skip ci]
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We cannot make the dataclass frozen when doing this, but this enables TF to automatically find trainable variables within GraphNetworkLayer objects which means we can get rid of the somewhat hacky _get_trainable_variables function that subclasses were supposed to override. This successfully trains models that would otherwise fail to converge if none of the graph layers were trainable. This closes google#323. Reviewers: ondrasej Pull Request: google#337
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This patch provides a custom implementation of trainable_variables in gnn_model_base. Theoretically this should have been made unnecessary by \google#337, but the interanl version of Tensorflow refuses to recurse into the modules inside of the GraphNetworkLayer classes. This patch fixes that by just returning the values regardless.
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This patch provides a custom implementation of trainable_variables in gnn_model_base. Theoretically this should have been made unnecessary by \google#337, but the interanl version of Tensorflow refuses to recurse into the modules inside of the GraphNetworkLayer classes. This patch fixes that by just returning the values regardless. Pull Request: google#341
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This patch provides a custom implementation of trainable_variables in gnn_model_base. Theoretically this should have been made unnecessary by \#337, but the interanl version of Tensorflow refuses to recurse into the modules inside of the GraphNetworkLayer classes. This patch fixes that by just returning the values regardless. Reviewers: orodley, virajbshah, ondrasej Reviewed By: ondrasej Pull Request: #341
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We cannot make the dataclass frozen when doing this, but this enables TF
to automatically find trainable variables within GraphNetworkLayer
objects which means we can get rid of the somewhat hacky
_get_trainable_variables function that subclasses were supposed to
override. This successfully trains models that would otherwise fail to
converge if none of the graph layers were trainable.
This closes #323.