Comments (4)
Hi Jonas, nice spot! Are you using the most recent versions of both branches?
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I found out the reason why that happens even though defaults are the same across branches. Development will allow the use of residual coupling blocks, which outsourced the output layer of the block outside the internal Sequential
network. TensorFlow does not know how to assign the weights for that new layer after loading. I will re-write the addition in a backward-compatible manner.
from bayesflow.
I pushed a preliminary fix. Could you try to reproduce the bug with the current state of the Development branch? :)
from bayesflow.
The preliminary fix works nicely. Issue seems to be solved!
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Related Issues (20)
- Change Support / Acknowledgements HOT 1
- Publish as conda installable package
- Parallelize Test Workflows HOT 2
- `test_time_series_transformer` occasionally fails
- Make heavier use of `pytest.fixture`
- Diagnostic plots do not do so well with simple (one-parameter) models HOT 2
- Remove code duplication from diagnostics module HOT 1
- Add tests for model comparison
- Links in the table of contents of the example notebooks do not work
- Dependency problems HOT 1
- Backport dependency fixes to releases/master HOT 1
- pip install v1.1.5 fails on Mac (M1) HOT 1
- OOM after ~ 50 epochs HOT 10
- bayesflow breaks existing tensorflow installation HOT 5
- Affine coupling flows underperforming with current settings on streamlined-backend
- OfflineDataset should not require both batch_size and batches_per_epoch
- Loss not shown in keras output HOT 4
- streamlined-backend DeepSet
- Implement LSTMNet for time series embedding
- InvertibleNetwork error (Input 0 of layer "dense" is incompatible with the layer) HOT 1
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