Comments (2)
Yes, there is a backward compatibility issue introduced around tensorflow 2.14. I wanted to take a look at handling this, but for now I would recommend downgrading. The newest tensorflow versions are fishy anyway, as I've recently found that starting with 2.10 there are memory leaks in certain scenarios (in MoLeR's case, they seem to appear when repeatedly encoding a lot of molecules in a loop). Neither the compatibility issues nor the potential leaks happen in 2.9, so I would recommend that version unless you really need to use a newer one.
from molecule-generation.
Related Issues (20)
- How does decode can return multiple similar molecules? HOT 2
- Motif embeddings HOT 2
- Clarification: correct_edge_choices is array of all zeros, while valid_edge_choices has a few candidates HOT 3
- Script for recreating evaluation scores on Guacamol benchmark HOT 14
- Query about data split! HOT 1
- Question about node_type_predictor_class_loss_weight_factor HOT 2
- where is the training datasets? HOT 2
- how can i generate large SMILES ? for example generate 100000000? HOT 1
- IndexError: pop from empty list HOT 11
- Data Preprocessing HOT 1
- Warning when using Load_model_from_directory(dir) HOT 1
- Tensorflow warnings when using encode HOT 5
- Large amount of error messages when using decode HOT 5
- libdevice not found during training using default conda environment on Ubuntu 22.04.2 with a RTX A4000 HOT 4
- Computing likely next actions HOT 3
- Optimising latent vectors for objective HOT 4
- memory overflow with large dataset preprocessing HOT 4
- preprocess need too many time AND how use my csv to generate the train.smiles and valid.smiles HOT 5
- advices of the evaluation options defined by sklearn.metrics HOT 1
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from molecule-generation.