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ran more test training.
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@PatReis
PatReis committed Dec 12, 2023
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3 changes: 3 additions & 0 deletions README.md
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Expand Up @@ -301,6 +301,9 @@ You can find a [table](training/results/README.md) of common benchmark datasets
Some known issues to be aware of, if using and making new models or layers with `kgcnn`.
* Jagged or nested Tensors loading into models for PyTorch backend is not working.
* Dataloader for Jax is not yet implemented.
* ForceModel does not support all tensor types and does not have Scaler layer yet.
* BatchNormalization layer dos not support padding yet.


<a name="citing"></a>
# Citing
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67 changes: 67 additions & 0 deletions training/hyper/hyper_freesolv.py
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Expand Up @@ -967,4 +967,71 @@
"kgcnn_version": "4.0.0"
}
},
"RGCN": {
"model": {
"class_name": "make_model",
"module_name": "kgcnn.literature.RGCN",
"config": {
"name": "RGCN",
"inputs": [
{"shape": [None, 41], "name": "node_attributes", "dtype": "float32"},
{"shape": [None, 1], "name": "edge_weights", "dtype": "float32"},
{"shape": [None], "name": "edge_number", "dtype": "int64"},
{"shape": [None, 2], "name": "edge_indices", "dtype": "int64"},
{"shape": (), "name": "total_nodes", "dtype": "int64"},
{"shape": (), "name": "total_edges", "dtype": "int64"},
],
"input_embedding": None,
"input_node_embedding": {"input_dim": 95, "output_dim": 64},
"dense_relation_kwargs": {"units": 64, "num_relations": 20},
"dense_kwargs": {"units": 64},
"activation_kwargs": {"activation": "swish"},
"depth": 5, "verbose": 10,
"output_embedding": "graph",
"output_mlp": {"use_bias": [True, True, False], "units": [140, 70, 1],
"activation": ["relu", "relu", "linear"]},
}
},
"training": {
"fit": {
"batch_size": 32,
"epochs": 800,
"validation_freq": 10,
"verbose": 2,
"callbacks": [
{
"class_name": "kgcnn>LinearLearningRateScheduler", "config": {
"learning_rate_start": 1e-03, "learning_rate_stop": 5e-05, "epo_min": 250, "epo": 800,
"verbose": 0}
}
]
},
"compile": {
"optimizer": {"class_name": "Adam", "config": {"learning_rate": 1e-03}},
"loss": "mean_absolute_error"
},
"cross_validation": {"class_name": "KFold",
"config": {"n_splits": 5, "random_state": 42, "shuffle": True}},
"scaler": {"class_name": "StandardLabelScaler",
"config": {"with_std": True, "with_mean": True, "copy": True}},
},
"data": {
"dataset": {
"class_name": "FreeSolvDataset",
"module_name": "kgcnn.data.datasets.FreeSolvDataset",
"config": {},
"methods": [
{"set_attributes": {}},
{"map_list": {"method": "normalize_edge_weights_sym"}},
{"map_list": {"method": "count_nodes_and_edges"}}
]
},
"data_unit": "mol/L"
},
"info": {
"postfix": "",
"postfix_file": "",
"kgcnn_version": "4.0.0"
}
},
}
155 changes: 155 additions & 0 deletions training/results/ESOLDataset/MAT/MAT_ESOLDataset_score.yaml
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OS: posix_linux
backend: tensorflow
cuda_available: 'True'
data_unit: mol/L
date_time: '2023-12-12 16:09:31'
device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'',
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device_memory: '[]'
device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]'
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1 change: 1 addition & 0 deletions training/results/ESOLDataset/MAT/MAT_hyper.json
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155 changes: 155 additions & 0 deletions training/results/ESOLDataset/MEGAN/MEGAN_ESOLDataset_score.yaml
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OS: posix_linux
backend: tensorflow
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data_unit: mol/L
date_time: '2023-12-12 16:22:43'
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device_memory: '[]'
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