Closed plhijk closed 1 year ago
Thank you! Where are you seeing 2. fail for tensor flow? I re-ran the wildcard tests on PR #44 and it seems to be working fine at least with python 3.8 and TF 2.? (currently rerunning py 3.7 and TF 2.*). If possible I'd like to keep the wildcard in because the goal is to keep PsychRNN forward compatible with new versions of Tensorflow.
Merging #47 (db06dfd) into master (ea66983) will not change coverage. The diff coverage is
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I was just concerned about incompatibility, e.g. TF v2.11 changed tf.keras.optimizers.Optimizer
class (see https://github.com/tensorflow/tensorflow/releases/tag/v2.11.0). Not sure how much it affects the implementation in this package. The test with py3.8 and TF2.11 passed so it seems ok.
I was just concerned about incompatibility, e.g. TF v2.11 changed
tf.keras.optimizers.Optimizer
class (see https://github.com/tensorflow/tensorflow/releases/tag/v2.11.0). Not sure how much it affects the implementation in this package. The test with py3.8 and TF2.11 passed so it seems ok.
The default optimizer in psychrnn is tf.compat.v1.train.AdamOptimizer so it should keep working fine (and tests that use it pass) so I'm going to keep the wildcard in. Thanks for the PR!
Recent numpy 1.24 deprecated some apis, breaking compatibilty with TF. So I added a constrain to numpy version in tests. I also removed the wildcard for TF v2, since recent versions seem to introduce some breaking changes.