Comments (2)
OK, I have a fix for this. For M1 Macs you need to install Tensorflow from the source -
e.g. one of these https://drive.google.com/drive/folders/11cACNiynhi45br1aW3ub5oQ6SrDEQx4p
Now it works as expected, I think...
molecule_generation sample MODEL_DIR 10
Loading a trained model from: MODEL_DIR/GNN_Edge_MLP_MoLeR__2022-02-24_07-16-23_best.pkl
2022-08-31 12:03:19,747 trace_dataset.py:44 INFO Initialising TraceDataset.
2022-08-31 12:03:19,748 trace_dataset.py:44 INFO Initialising TraceDataset.
2022-08-31 12:03:19,756 trace_dataset.py:44 INFO Initialising TraceDataset.
2022-08-31 12:03:19,764 trace_dataset.py:44 INFO Initialising TraceDataset.
2022-08-31 12:03:19,767 trace_dataset.py:44 INFO Initialising TraceDataset.
2022-08-31 12:03:19,775 trace_dataset.py:44 INFO Initialising TraceDataset.
O=C1C2=CC=C(C3=CC=CC=C3)C=C=C2OC2=CC=CC=C12
CC(=O)NC1=NC2=CC(OCC3=CC=CN(CC4=CC=C(Cl)C=C4)C3=O)=CC=C2N1
CCN1C(=O)C2=CC=CC=C2N=C1NC(C)C(=O)NCC(=O)N=[N+]=[N-]
CC(=O)N1CCCC1C1=NC2=CC=C(C(C)(C)CCCC(C)C)C=C2NC1=NC1=CC=C(O)C=C1
N=C(N)NCCCCOC1=CC=C(Br)C(Cl)=N1
O=C1C2=CC=C(C3=NN=CO3)C=C2N=CN1CC1=CC=C(C2=CC=CC=C2)C=C1
O=CCCCCCN1C=CC2=CC=CC=C21
CCOC(=O)C1=CC2=CC(CC(C)C)=CC=C2N=C1C1=CC=C(Br)C=C1
CC1=C(C#N)C=C(NC(=O)NC2CCCCC2)N1CC#N
CC1=CNN=C1NC(=O)COC1=CC=C(Cl)C=C1Cl
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Hey @MKCarter - indeed, tensorflow
installation may differ on macs, and the output you got looks right (in fact, I'm getting exactly same molecules on Linux, so it's good that things are deterministic across platforms). I will mention this discrepancy in the README.
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Related Issues (20)
- Pre-training model download link failed HOT 6
- 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
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