From 3569223c6d0a58035f7257fcda1d3de5b3b5d0f5 Mon Sep 17 00:00:00 2001 From: PatReis Date: Mon, 25 Mar 2024 09:57:30 +0100 Subject: [PATCH] Updated training results --- .../Megnet_MatProjectEFormDataset_score.yaml | 65 +++++ .../Megnet_hyper.json | 1 + .../PAiNN_MatProjectEFormDataset_score.yaml | 157 ++++++++++++ .../PAiNN_make_crystal_model/PAiNN_hyper.json | 1 + .../DimeNetPP_QM9Dataset_score_G.yaml | 139 +++++++++++ .../DimeNetPP_QM9Dataset_score_H.yaml | 79 ++++++ .../DimeNetPP_QM9Dataset_score_HOMO.yaml | 79 ++++++ .../DimeNetPP_QM9Dataset_score_LUMO.yaml | 139 +++++++++++ .../DimeNetPP/DimeNetPP_hyper_G.json | 1 + .../DimeNetPP/DimeNetPP_hyper_H.json | 1 + .../DimeNetPP/DimeNetPP_hyper_HOMO.json | 1 + .../DimeNetPP/DimeNetPP_hyper_LUMO.json | 1 + .../EGNN/EGNN_QM9Dataset_score_G.yaml | 157 ++++++++++++ .../EGNN/EGNN_QM9Dataset_score_H.yaml | 88 +++++++ .../EGNN/EGNN_QM9Dataset_score_HOMO.yaml | 88 +++++++ .../EGNN/EGNN_QM9Dataset_score_LUMO.yaml | 157 ++++++++++++ .../results/QM9Dataset/EGNN/EGNN_hyper_G.json | 1 + .../results/QM9Dataset/EGNN/EGNN_hyper_H.json | 1 + .../QM9Dataset/EGNN/EGNN_hyper_HOMO.json | 1 + .../QM9Dataset/EGNN/EGNN_hyper_LUMO.json | 1 + .../MXMNet/MXMNet_QM9Dataset_score_G.yaml | 229 ++++++++++++++++++ .../MXMNet/MXMNet_QM9Dataset_score_H.yaml | 124 ++++++++++ .../MXMNet/MXMNet_QM9Dataset_score_HOMO.yaml | 124 ++++++++++ .../MXMNet/MXMNet_QM9Dataset_score_LUMO.yaml | 229 ++++++++++++++++++ .../QM9Dataset/MXMNet/MXMNet_hyper_G.json | 1 + .../QM9Dataset/MXMNet/MXMNet_hyper_H.json | 1 + .../QM9Dataset/MXMNet/MXMNet_hyper_HOMO.json | 1 + .../QM9Dataset/MXMNet/MXMNet_hyper_LUMO.json | 1 + .../Megnet/Megnet_QM9Dataset_score_H.yaml | 88 +++++++ .../Megnet/Megnet_QM9Dataset_score_HOMO.yaml | 88 +++++++ .../QM9Dataset/Megnet/Megnet_hyper_H.json | 1 + .../QM9Dataset/Megnet/Megnet_hyper_HOMO.json | 1 + .../NMPN/NMPN_QM9Dataset_score_G.yaml | 157 ++++++++++++ .../NMPN/NMPN_QM9Dataset_score_H.yaml | 88 +++++++ .../NMPN/NMPN_QM9Dataset_score_HOMO.yaml | 88 +++++++ .../NMPN/NMPN_QM9Dataset_score_LUMO.yaml | 157 ++++++++++++ .../results/QM9Dataset/NMPN/NMPN_hyper_G.json | 1 + .../results/QM9Dataset/NMPN/NMPN_hyper_H.json | 1 + .../QM9Dataset/NMPN/NMPN_hyper_HOMO.json | 1 + .../QM9Dataset/NMPN/NMPN_hyper_LUMO.json | 1 + 40 files changed, 2540 insertions(+) create mode 100644 training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_MatProjectEFormDataset_score.yaml create mode 100644 training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_hyper.json create mode 100644 training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_MatProjectEFormDataset_score.yaml create mode 100644 training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_hyper.json create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_G.yaml create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_H.yaml create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_HOMO.yaml create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_LUMO.yaml create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_G.json create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_H.json create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_HOMO.json create mode 100644 training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_LUMO.json create mode 100644 training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_G.yaml create mode 100644 training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_H.yaml create mode 100644 training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_HOMO.yaml create mode 100644 training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_LUMO.yaml create mode 100644 training/results/QM9Dataset/EGNN/EGNN_hyper_G.json create mode 100644 training/results/QM9Dataset/EGNN/EGNN_hyper_H.json create mode 100644 training/results/QM9Dataset/EGNN/EGNN_hyper_HOMO.json create mode 100644 training/results/QM9Dataset/EGNN/EGNN_hyper_LUMO.json create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_G.yaml create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_H.yaml create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_HOMO.yaml create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_LUMO.yaml create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_hyper_G.json create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_hyper_H.json create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_hyper_HOMO.json create mode 100644 training/results/QM9Dataset/MXMNet/MXMNet_hyper_LUMO.json create mode 100644 training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_H.yaml create mode 100644 training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_HOMO.yaml create mode 100644 training/results/QM9Dataset/Megnet/Megnet_hyper_H.json create mode 100644 training/results/QM9Dataset/Megnet/Megnet_hyper_HOMO.json create mode 100644 training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_G.yaml create mode 100644 training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_H.yaml create mode 100644 training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_HOMO.yaml create mode 100644 training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_LUMO.yaml create mode 100644 training/results/QM9Dataset/NMPN/NMPN_hyper_G.json create mode 100644 training/results/QM9Dataset/NMPN/NMPN_hyper_H.json create mode 100644 training/results/QM9Dataset/NMPN/NMPN_hyper_HOMO.json create mode 100644 training/results/QM9Dataset/NMPN/NMPN_hyper_LUMO.json diff --git a/training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_MatProjectEFormDataset_score.yaml b/training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_MatProjectEFormDataset_score.yaml new file mode 100644 index 00000000..2114248e --- /dev/null +++ b/training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_MatProjectEFormDataset_score.yaml @@ -0,0 +1,65 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: eV/atom +date_time: '2024-03-01 15:58:56' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (8, 0), ''device_name'': ''NVIDIA A100 + 80GB PCIe''}]' +epochs: +- 1000 +execute_folds: +- 0 +kgcnn_version: 4.0.1 +learning_rate: +- 5.549999968934571e-06 +loss: +- 0.004087877459824085 +max_learning_rate: +- 0.0005000000237487257 +max_loss: +- 0.24293601512908936 +max_scaled_mean_absolute_error: +- 0.28267571330070496 +max_scaled_root_mean_squared_error: +- 0.46570608019828796 +max_val_loss: +- 0.08681195974349976 +max_val_scaled_mean_absolute_error: +- 0.10100412368774414 +max_val_scaled_root_mean_squared_error: +- 0.18269585072994232 +min_learning_rate: +- 5.549999968934571e-06 +min_loss: +- 0.004087877459824085 +min_scaled_mean_absolute_error: +- 0.0047565787099301815 +min_scaled_root_mean_squared_error: +- 0.012960121035575867 +min_val_loss: +- 0.023371437564492226 +min_val_scaled_mean_absolute_error: +- 0.027191713452339172 +min_val_scaled_root_mean_squared_error: +- 0.06932297348976135 +model_class: make_crystal_model +model_name: Megnet +model_version: '2023-12-05' +multi_target_indices: null +number_histories: 1 +scaled_mean_absolute_error: +- 0.0047565787099301815 +scaled_root_mean_squared_error: +- 0.012960121035575867 +seed: 42 +time_list: +- 1 day, 23:14:50.795494 +val_loss: +- 0.023371437564492226 +val_scaled_mean_absolute_error: +- 0.027191713452339172 +val_scaled_root_mean_squared_error: +- 0.07003486901521683 diff --git a/training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_hyper.json b/training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_hyper.json new file mode 100644 index 00000000..dac0f7ba --- /dev/null +++ b/training/results/MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_hyper.json @@ -0,0 +1 @@ +{"model": {"module_name": "kgcnn.literature.Megnet", "class_name": "make_crystal_model", "config": {"name": "Megnet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int32", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [1], "name": "charge", "dtype": "float32", "ragged": false}, {"shape": [null, 3], "name": "range_image", "dtype": "int64", "ragged": true}, {"shape": [3, 3], "name": "graph_lattice", "dtype": "float32", "ragged": false}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "make_distance": true, "expand_distance": true, "gauss_args": {"bins": 25, "distance": 5, "offset": 0.0, "sigma": 0.4}, "meg_block_args": {"node_embed": [64, 32, 32], "edge_embed": [64, 32, 32], "env_embed": [64, 32, 32], "activation": "kgcnn>softplus2"}, "set2set_args": {"channels": 16, "T": 3, "pooling_method": "sum", "init_qstar": "0"}, "node_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "edge_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "state_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "nblocks": 3, "has_ff": true, "dropout": null, "use_set2set": true, "verbose": 10, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true, true], "units": [32, 16, 1], "activation": ["kgcnn>softplus2", "kgcnn>softplus2", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 1000, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0005, "learning_rate_stop": 5e-06, "epo_min": 100, "epo": 1000, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "StandardLabelScaler", "module_name": "kgcnn.data.transform.scaler.standard", "config": {"with_std": true, "with_mean": true, "copy": true}}, "multi_target_indices": null}, "data": {"data_unit": "eV/atom"}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "MatProjectEFormDataset", "module_name": "kgcnn.data.datasets.MatProjectEFormDataset", "config": {}, "methods": [{"map_list": {"method": "set_range_periodic", "max_distance": 5.0}}]}} \ No newline at end of file diff --git a/training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_MatProjectEFormDataset_score.yaml b/training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_MatProjectEFormDataset_score.yaml new file mode 100644 index 00000000..c56e61c5 --- /dev/null +++ b/training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_MatProjectEFormDataset_score.yaml @@ -0,0 +1,157 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: eV/atom +date_time: '2024-03-14 11:26:33' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (8, 0), ''device_name'': ''NVIDIA A100 + 80GB PCIe''}]' +epochs: +- 800 +- 800 +- 800 +- 800 +- 800 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +learning_rate: +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +loss: +- 0.0022031376138329506 +- 0.002156742848455906 +- 0.0021591256372630596 +- 0.002236530650407076 +- 0.002121950266882777 +max_learning_rate: +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +max_loss: +- 283.341552734375 +- 0.21523690223693848 +- 1.1116989850997925 +- 16.761430740356445 +- 0.4403814375400543 +max_scaled_mean_absolute_error: +- 329.690185546875 +- 0.25008291006088257 +- 1.2937359809875488 +- 19.515050888061523 +- 0.5118441581726074 +max_scaled_root_mean_squared_error: +- 107279.140625 +- 5.095092296600342 +- 411.5854187011719 +- 6322.42333984375 +- 105.53013610839844 +max_val_loss: +- 0.08015388250350952 +- 0.08247227221727371 +- 0.07635330408811569 +- 0.07353736460208893 +- 0.09253674000501633 +max_val_scaled_mean_absolute_error: +- 0.09318814426660538 +- 0.09583482146263123 +- 0.0888148844242096 +- 0.0855850875377655 +- 0.10754433274269104 +max_val_scaled_root_mean_squared_error: +- 0.2518233060836792 +- 0.15024664998054504 +- 3.1640191078186035 +- 0.136776402592659 +- 0.34822598099708557 +min_learning_rate: +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +- 1.0128571375389583e-05 +min_loss: +- 0.0022031376138329506 +- 0.002156742848455906 +- 0.0021591256372630596 +- 0.002236530650407076 +- 0.0021089049987494946 +min_scaled_mean_absolute_error: +- 0.0025635266210883856 +- 0.0025060451589524746 +- 0.0025124901439994574 +- 0.0026032738387584686 +- 0.0024510754738003016 +min_scaled_root_mean_squared_error: +- 0.0061350935138762 +- 0.006993255112320185 +- 0.00799341220408678 +- 0.00853035133332014 +- 0.008364901877939701 +min_val_loss: +- 0.020580174401402473 +- 0.021014703437685966 +- 0.020457644015550613 +- 0.020269570872187614 +- 0.020771024748682976 +min_val_scaled_mean_absolute_error: +- 0.023943014442920685 +- 0.02442334219813347 +- 0.023796744644641876 +- 0.023600205779075623 +- 0.02413744293153286 +min_val_scaled_root_mean_squared_error: +- 0.05723186582326889 +- 0.055046603083610535 +- 0.05682484433054924 +- 0.053158361464738846 +- 0.0530422143638134 +model_class: make_crystal_model +model_name: PAiNN +model_version: '2023-10-04' +multi_target_indices: null +number_histories: 5 +scaled_mean_absolute_error: +- 0.0025635266210883856 +- 0.0025060451589524746 +- 0.0025124901439994574 +- 0.0026032738387584686 +- 0.002464835997670889 +scaled_root_mean_squared_error: +- 0.0061350935138762 +- 0.006993255112320185 +- 0.00799341220408678 +- 0.00853035133332014 +- 0.008378282189369202 +seed: 42 +time_list: +- '15:06:54.175667' +- '15:23:58.313253' +- '15:37:53.102961' +- '16:02:28.792165' +- '15:41:58.027323' +val_loss: +- 0.020749464631080627 +- 0.021014703437685966 +- 0.020489152520895004 +- 0.020450659096240997 +- 0.021013258025050163 +val_scaled_mean_absolute_error: +- 0.02413973957300186 +- 0.02442334219813347 +- 0.02383369207382202 +- 0.02381002902984619 +- 0.024419881403446198 +val_scaled_root_mean_squared_error: +- 0.05800706893205643 +- 0.05669984221458435 +- 0.05682484433054924 +- 0.05372071638703346 +- 0.05350622162222862 diff --git a/training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_hyper.json b/training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_hyper.json new file mode 100644 index 00000000..5742d05c --- /dev/null +++ b/training/results/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_hyper.json @@ -0,0 +1 @@ +{"model": {"module_name": "kgcnn.literature.PAiNN", "class_name": "make_crystal_model", "config": {"name": "PAiNN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int32", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "range_image", "dtype": "int64", "ragged": true}, {"shape": [3, 3], "name": "graph_lattice", "dtype": "float32", "ragged": false}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128}, "bessel_basis": {"num_radial": 20, "cutoff": 5.0, "envelope_exponent": 5}, "equiv_initialize_kwargs": {"dim": 3, "method": "eye"}, "pooling_args": {"pooling_method": "mean"}, "conv_args": {"units": 128, "cutoff": null, "conv_pool": "sum"}, "update_args": {"units": 128}, "depth": 3, "verbose": 10, "equiv_normalization": true, "node_normalization": false, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true], "units": [128, 1], "activation": ["swish", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 800, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0001, "learning_rate_stop": 1e-05, "epo_min": 100, "epo": 800, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "StandardLabelScaler", "module_name": "kgcnn.data.transform.scaler.standard", "config": {"with_std": true, "with_mean": true, "copy": true}}, "multi_target_indices": null}, "data": {"data_unit": "eV/atom"}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "MatProjectEFormDataset", "module_name": "kgcnn.data.datasets.MatProjectEFormDataset", "config": {}, "methods": [{"map_list": {"method": "set_range_periodic", "max_distance": 5.0}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_G.yaml b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_G.yaml new file mode 100644 index 00000000..d3abadd6 --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_G.yaml @@ -0,0 +1,139 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-15 06:23:23' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 600 +- 600 +- 600 +- 600 +- 600 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +loss: +- 0.002868798328563571 +- 0.0028493432328104973 +- 0.002786037977784872 +- 0.003136424347758293 +- 0.0028916450683027506 +max_loss: +- 0.17670932412147522 +- 0.17715905606746674 +- 0.17726151645183563 +- 0.17782989144325256 +- 0.17748409509658813 +max_scaled_mean_absolute_error: +- 0.1939270794391632 +- 0.19473996758460999 +- 0.19480031728744507 +- 0.19551712274551392 +- 0.19523854553699493 +max_scaled_root_mean_squared_error: +- 0.33319583535194397 +- 0.33428409695625305 +- 0.3362830877304077 +- 0.3380829691886902 +- 0.3377641439437866 +max_val_loss: +- 0.029520221054553986 +- 0.02837827056646347 +- 0.027989225462079048 +- 0.033230170607566833 +- 0.030747266486287117 +max_val_scaled_mean_absolute_error: +- 0.03224688395857811 +- 0.03109075129032135 +- 0.030618516728281975 +- 0.036430105566978455 +- 0.03372833505272865 +max_val_scaled_root_mean_squared_error: +- 0.05694420635700226 +- 0.05150516703724861 +- 0.05387285351753235 +- 0.062075868248939514 +- 0.056977249681949615 +min_loss: +- 0.002744976431131363 +- 0.0027860943228006363 +- 0.002786037977784872 +- 0.0029745546635240316 +- 0.00281642097979784 +min_scaled_mean_absolute_error: +- 0.0030123277101665735 +- 0.003062501084059477 +- 0.003061545779928565 +- 0.0032703846227377653 +- 0.0030979991424828768 +min_scaled_root_mean_squared_error: +- 0.0041679153218865395 +- 0.004196802619844675 +- 0.004170420579612255 +- 0.004524059593677521 +- 0.004268138203769922 +min_val_loss: +- 0.00979491788893938 +- 0.009362481534481049 +- 0.009580131620168686 +- 0.009070448577404022 +- 0.009656858630478382 +min_val_scaled_mean_absolute_error: +- 0.01063892338424921 +- 0.010197021067142487 +- 0.010442499071359634 +- 0.009880520403385162 +- 0.01054394617676735 +min_val_scaled_root_mean_squared_error: +- 0.03518393263220787 +- 0.03090721182525158 +- 0.03250584751367569 +- 0.03193874657154083 +- 0.030954917892813683 +model_class: make_model +model_name: DimeNetPP +model_version: '2023-12-04' +multi_target_indices: +- 13 +number_histories: 5 +scaled_mean_absolute_error: +- 0.0031482786871492863 +- 0.0031320941634476185 +- 0.003061545779928565 +- 0.0034483373165130615 +- 0.003180810948833823 +scaled_root_mean_squared_error: +- 0.004328957758843899 +- 0.004300135187804699 +- 0.004170420579612255 +- 0.0047387913800776005 +- 0.004378628917038441 +seed: 42 +time_list: +- 1 day, 10:52:05.406221 +- 1 day, 8:18:09.581010 +- 1 day, 10:18:30.398631 +- 1 day, 10:47:51.329256 +- 1 day, 11:13:00.770296 +val_loss: +- 0.01041325181722641 +- 0.010219499468803406 +- 0.009580131620168686 +- 0.009070448577404022 +- 0.010097221471369267 +val_scaled_mean_absolute_error: +- 0.011316145770251751 +- 0.011138662695884705 +- 0.010442499071359634 +- 0.009880520403385162 +- 0.0110316826030612 +val_scaled_root_mean_squared_error: +- 0.03608761727809906 +- 0.03144918009638786 +- 0.03252306580543518 +- 0.03256518766283989 +- 0.03239353746175766 diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_H.yaml b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_H.yaml new file mode 100644 index 00000000..a1488c9e --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_H.yaml @@ -0,0 +1,79 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-25 04:26:18' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 600 +- 600 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +loss: +- 0.0030547985807061195 +- 0.0031594946049153805 +max_loss: +- 0.18200941383838654 +- 0.18039806187152863 +max_scaled_mean_absolute_error: +- 0.19871577620506287 +- 0.1971033662557602 +max_scaled_root_mean_squared_error: +- 0.3368868827819824 +- 0.3338407874107361 +max_val_loss: +- 0.026954108849167824 +- 0.031196754425764084 +max_val_scaled_mean_absolute_error: +- 0.029313750565052032 +- 0.03397288918495178 +max_val_scaled_root_mean_squared_error: +- 0.05636412277817726 +- 0.059521134942770004 +min_loss: +- 0.0026739316526800394 +- 0.0028502270579338074 +min_scaled_mean_absolute_error: +- 0.0029193637892603874 +- 0.0031141082290560007 +min_scaled_root_mean_squared_error: +- 0.004010347183793783 +- 0.004236821085214615 +min_val_loss: +- 0.008577571250498295 +- 0.008637722581624985 +min_val_scaled_mean_absolute_error: +- 0.009276311844587326 +- 0.009365281090140343 +min_val_scaled_root_mean_squared_error: +- 0.03182884305715561 +- 0.032217614352703094 +model_class: make_model +model_name: DimeNetPP +model_version: '2023-12-04' +multi_target_indices: +- 12 +number_histories: 2 +scaled_mean_absolute_error: +- 0.003335044253617525 +- 0.0034518814645707607 +scaled_root_mean_squared_error: +- 0.00451664999127388 +- 0.004660776816308498 +seed: 42 +time_list: +- 1 day, 10:50:05.903201 +- 1 day, 12:02:47.644100 +val_loss: +- 0.009079432114958763 +- 0.009007935412228107 +val_scaled_mean_absolute_error: +- 0.009823390282690525 +- 0.009767130017280579 +val_scaled_root_mean_squared_error: +- 0.0325394906103611 +- 0.032723262906074524 diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_HOMO.yaml b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_HOMO.yaml new file mode 100644 index 00000000..56ea1212 --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_HOMO.yaml @@ -0,0 +1,79 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-24 18:18:12' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 600 +- 600 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +loss: +- 0.005621917080134153 +- 0.00561577919870615 +max_loss: +- 0.3294568657875061 +- 0.33053842186927795 +max_scaled_mean_absolute_error: +- 0.19851791858673096 +- 0.19896641373634338 +max_scaled_root_mean_squared_error: +- 0.2926676273345947 +- 0.29251179099082947 +max_val_loss: +- 0.10414715856313705 +- 0.1003655269742012 +max_val_scaled_mean_absolute_error: +- 0.06269031018018723 +- 0.06037885695695877 +max_val_scaled_root_mean_squared_error: +- 0.09375123679637909 +- 0.08823802322149277 +min_loss: +- 0.005608322098851204 +- 0.005492119584232569 +min_scaled_mean_absolute_error: +- 0.0033793302718549967 +- 0.0033058912958949804 +min_scaled_root_mean_squared_error: +- 0.0046747042797505856 +- 0.0046160463243722916 +min_val_loss: +- 0.04799223318696022 +- 0.04828876256942749 +min_val_scaled_mean_absolute_error: +- 0.02886221557855606 +- 0.029051648452878 +min_val_scaled_root_mean_squared_error: +- 0.04968913644552231 +- 0.05029897391796112 +model_class: make_model +model_name: DimeNetPP +model_version: '2023-12-04' +multi_target_indices: +- 5 +number_histories: 2 +scaled_mean_absolute_error: +- 0.0033875287044793367 +- 0.0033803347032517195 +scaled_root_mean_squared_error: +- 0.004697032272815704 +- 0.004739508498460054 +seed: 42 +time_list: +- 1 day, 9:46:40.387562 +- 1 day, 9:59:13.148100 +val_loss: +- 0.04799223318696022 +- 0.04854681342840195 +val_scaled_mean_absolute_error: +- 0.02886221557855606 +- 0.029207797721028328 +val_scaled_root_mean_squared_error: +- 0.0497230626642704 +- 0.050450123846530914 diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_LUMO.yaml b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_LUMO.yaml new file mode 100644 index 00000000..e200d629 --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_LUMO.yaml @@ -0,0 +1,139 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-15 05:37:18' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 600 +- 600 +- 600 +- 600 +- 600 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +loss: +- 0.003047335660085082 +- 0.003094656625762582 +- 0.0030088669154793024 +- 0.0032373573631048203 +- 0.0031546459067612886 +max_loss: +- 0.23500996828079224 +- 0.2366899847984314 +- 0.23607957363128662 +- 0.22435347735881805 +- 0.22883784770965576 +max_scaled_mean_absolute_error: +- 0.29981258511543274 +- 0.30241578817367554 +- 0.30178582668304443 +- 0.28631457686424255 +- 0.29248374700546265 +max_scaled_root_mean_squared_error: +- 0.4665443003177643 +- 0.46518009901046753 +- 0.4703861176967621 +- 0.45145437121391296 +- 0.45497927069664 +max_val_loss: +- 0.04899614304304123 +- 0.04892812296748161 +- 0.04509369283914566 +- 0.04651492461562157 +- 0.04576588422060013 +max_val_scaled_mean_absolute_error: +- 0.062388934195041656 +- 0.062459610402584076 +- 0.057511452585458755 +- 0.05924895405769348 +- 0.05839836224913597 +max_val_scaled_root_mean_squared_error: +- 0.0909452885389328 +- 0.09045080840587616 +- 0.08785920590162277 +- 0.09141668677330017 +- 0.09036029130220413 +min_loss: +- 0.0029890381265431643 +- 0.0030494313687086105 +- 0.0029814427252858877 +- 0.0032254045363515615 +- 0.003066680394113064 +min_scaled_mean_absolute_error: +- 0.003813197836279869 +- 0.003896087408065796 +- 0.003811137517914176 +- 0.004116182681173086 +- 0.003919476177543402 +min_scaled_root_mean_squared_error: +- 0.005143431015312672 +- 0.005263163708150387 +- 0.005136983469128609 +- 0.005592863075435162 +- 0.005281213205307722 +min_val_loss: +- 0.02177516557276249 +- 0.021623849868774414 +- 0.021952800452709198 +- 0.021465566009283066 +- 0.021921390667557716 +min_val_scaled_mean_absolute_error: +- 0.027673283591866493 +- 0.02757641114294529 +- 0.02794693037867546 +- 0.027305183932185173 +- 0.027962036430835724 +min_val_scaled_root_mean_squared_error: +- 0.05317383259534836 +- 0.046188537031412125 +- 0.05156544968485832 +- 0.04909032583236694 +- 0.05773765966296196 +model_class: make_model +model_name: DimeNetPP +model_version: '2023-12-04' +multi_target_indices: +- 6 +number_histories: 5 +scaled_mean_absolute_error: +- 0.00388749479316175 +- 0.0039538051933050156 +- 0.003846280975267291 +- 0.004131362307816744 +- 0.004031961318105459 +scaled_root_mean_squared_error: +- 0.005271461326628923 +- 0.005349855404347181 +- 0.0051859300583601 +- 0.005625858902931213 +- 0.005435039754956961 +seed: 42 +time_list: +- 1 day, 11:37:30.406661 +- 1 day, 10:38:19.132694 +- 1 day, 11:08:35.997295 +- 1 day, 9:59:01.940146 +- 1 day, 10:30:57.868678 +val_loss: +- 0.022765742614865303 +- 0.021640418097376823 +- 0.022155018523335457 +- 0.022590167820453644 +- 0.022278789430856705 +val_scaled_mean_absolute_error: +- 0.028933990746736526 +- 0.027595946565270424 +- 0.028204066678881645 +- 0.028745461255311966 +- 0.028417527675628662 +val_scaled_root_mean_squared_error: +- 0.05436023324728012 +- 0.04624108225107193 +- 0.05187717452645302 +- 0.04985475167632103 +- 0.0582762211561203 diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_G.json b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_G.json new file mode 100644 index 00000000..2e61b330 --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_G.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.DimeNetPP", "config": {"name": "DimeNetPP", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "emb_size": 128, "out_emb_size": 256, "int_emb_size": 64, "basis_emb_size": 8, "num_blocks": 4, "num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5, "num_before_skip": 1, "num_after_skip": 2, "num_dense_output": 3, "num_targets": 1, "extensive": true, "output_init": "zeros", "activation": "swish", "verbose": 10, "output_embedding": "graph", "use_output_mlp": false, "output_mlp": {}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 600, "validation_freq": 10, "verbose": 2, "callbacks": []}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": {"class_name": "kgcnn>LinearWarmupExponentialDecay", "config": {"learning_rate": 0.001, "warmup_steps": 3000.0, "decay_steps": 4000000.0, "decay_rate": 0.01}}, "use_ema": true, "amsgrad": true}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularLabelScaler", "config": {}}]}}, "multi_target_indices": [13]}, "data": {}, "info": {"postfix": "", "postfix_file": "_G", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_H.json b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_H.json new file mode 100644 index 00000000..f42ec810 --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_H.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.DimeNetPP", "config": {"name": "DimeNetPP", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "emb_size": 128, "out_emb_size": 256, "int_emb_size": 64, "basis_emb_size": 8, "num_blocks": 4, "num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5, "num_before_skip": 1, "num_after_skip": 2, "num_dense_output": 3, "num_targets": 1, "extensive": true, "output_init": "zeros", "activation": "swish", "verbose": 10, "output_embedding": "graph", "use_output_mlp": false, "output_mlp": {}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 600, "validation_freq": 10, "verbose": 2, "callbacks": []}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": {"class_name": "kgcnn>LinearWarmupExponentialDecay", "config": {"learning_rate": 0.001, "warmup_steps": 3000.0, "decay_steps": 4000000.0, "decay_rate": 0.01}}, "use_ema": true, "amsgrad": true}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularLabelScaler", "config": {}}]}}, "multi_target_indices": [12]}, "data": {}, "info": {"postfix": "", "postfix_file": "_H", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_HOMO.json b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_HOMO.json new file mode 100644 index 00000000..369d5ceb --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_HOMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.DimeNetPP", "config": {"name": "DimeNetPP", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "emb_size": 128, "out_emb_size": 256, "int_emb_size": 64, "basis_emb_size": 8, "num_blocks": 4, "num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5, "num_before_skip": 1, "num_after_skip": 2, "num_dense_output": 3, "num_targets": 1, "extensive": false, "output_init": "zeros", "activation": "swish", "verbose": 10, "output_embedding": "graph", "use_output_mlp": false, "output_mlp": {}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 600, "validation_freq": 10, "verbose": 2, "callbacks": []}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": {"class_name": "kgcnn>LinearWarmupExponentialDecay", "config": {"learning_rate": 0.001, "warmup_steps": 3000.0, "decay_steps": 4000000.0, "decay_rate": 0.01}}, "use_ema": true, "amsgrad": true}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "StandardLabelScaler", "config": {"with_std": true, "with_mean": true, "copy": true}}]}}, "multi_target_indices": [5]}, "data": {}, "info": {"postfix": "", "postfix_file": "_HOMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_LUMO.json b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_LUMO.json new file mode 100644 index 00000000..80599cbf --- /dev/null +++ b/training/results/QM9Dataset/DimeNetPP/DimeNetPP_hyper_LUMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.DimeNetPP", "config": {"name": "DimeNetPP", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "emb_size": 128, "out_emb_size": 256, "int_emb_size": 64, "basis_emb_size": 8, "num_blocks": 4, "num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5, "num_before_skip": 1, "num_after_skip": 2, "num_dense_output": 3, "num_targets": 1, "extensive": false, "output_init": "zeros", "activation": "swish", "verbose": 10, "output_embedding": "graph", "use_output_mlp": false, "output_mlp": {}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 600, "validation_freq": 10, "verbose": 2, "callbacks": []}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": {"class_name": "kgcnn>LinearWarmupExponentialDecay", "config": {"learning_rate": 0.001, "warmup_steps": 3000.0, "decay_steps": 4000000.0, "decay_rate": 0.01}}, "use_ema": true, "amsgrad": true}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "StandardLabelScaler", "config": {"with_std": true, "with_mean": true, "copy": true}}]}}, "multi_target_indices": [6]}, "data": {}, "info": {"postfix": "", "postfix_file": "_LUMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_G.yaml b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_G.yaml new file mode 100644 index 00000000..908d812d --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_G.yaml @@ -0,0 +1,157 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-17 02:26:42' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 800 +- 800 +- 800 +- 800 +- 800 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +loss: +- 0.0013750848593190312 +- 0.0012069359654560685 +- 0.0014110677875578403 +- 0.001333847758360207 +- 0.0012733640614897013 +max_learning_rate: +- 0.0005000000237487257 +- 0.0005000000237487257 +- 0.0005000000237487257 +- 0.0005000000237487257 +- 0.0005000000237487257 +max_loss: +- 6.499157905578613 +- 4.966420650482178 +- 3.3079099655151367 +- 4.418166160583496 +- 3.28580379486084 +max_scaled_mean_absolute_error: +- 7.134607791900635 +- 5.460991382598877 +- 3.636110305786133 +- 4.859012126922607 +- 3.615441083908081 +max_scaled_root_mean_squared_error: +- 162.35934448242188 +- 100.12232971191406 +- 82.98491668701172 +- 96.47432708740234 +- 76.37738800048828 +max_val_loss: +- 0.6769805550575256 +- 0.6597443222999573 +- 0.681045651435852 +- 0.6639727354049683 +- 0.6514186859130859 +max_val_scaled_mean_absolute_error: +- 0.7428719997406006 +- 0.7251375913619995 +- 0.7483140230178833 +- 0.729958713054657 +- 0.7164338827133179 +max_val_scaled_root_mean_squared_error: +- 0.9515857696533203 +- 0.9387214779853821 +- 0.9466655850410461 +- 0.917400598526001 +- 0.9164226651191711 +min_learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +min_loss: +- 0.0013750848593190312 +- 0.0012069359654560685 +- 0.0014110677875578403 +- 0.001333847758360207 +- 0.0012733640614897013 +min_scaled_mean_absolute_error: +- 0.0015090825036168098 +- 0.0013269601622596383 +- 0.0015508926007896662 +- 0.0014666757779195905 +- 0.0014009468723088503 +min_scaled_root_mean_squared_error: +- 0.002416618401184678 +- 0.002156193135306239 +- 0.0024187511298805475 +- 0.0023223133757710457 +- 0.0021878869738429785 +min_val_loss: +- 0.010210053063929081 +- 0.009436402469873428 +- 0.010211406275629997 +- 0.009637052193284035 +- 0.009949469938874245 +min_val_scaled_mean_absolute_error: +- 0.011180663481354713 +- 0.010349894873797894 +- 0.011195708997547626 +- 0.010571632534265518 +- 0.010926742106676102 +min_val_scaled_root_mean_squared_error: +- 0.03608584776520729 +- 0.029965871945023537 +- 0.03994620218873024 +- 0.03372124210000038 +- 0.033663664013147354 +model_class: make_model +model_name: EGNN +model_version: '2023-12-04' +multi_target_indices: +- 13 +number_histories: 5 +scaled_mean_absolute_error: +- 0.0015090825036168098 +- 0.0013269601622596383 +- 0.0015508926007896662 +- 0.0014666757779195905 +- 0.0014009468723088503 +scaled_root_mean_squared_error: +- 0.002416618401184678 +- 0.002156193135306239 +- 0.0024187511298805475 +- 0.0023223133757710457 +- 0.0021878869738429785 +seed: 42 +time_list: +- '9:12:49.702726' +- '9:05:48.378793' +- '9:26:21.909456' +- '9:04:20.677346' +- '8:57:33.075461' +val_loss: +- 0.010210350155830383 +- 0.009436402469873428 +- 0.010211466811597347 +- 0.009637333452701569 +- 0.009949862957000732 +val_scaled_mean_absolute_error: +- 0.011180984787642956 +- 0.010349894873797894 +- 0.011195769533514977 +- 0.010571936145424843 +- 0.010927173309028149 +val_scaled_root_mean_squared_error: +- 0.036254364997148514 +- 0.03044840507209301 +- 0.04004671052098274 +- 0.035204432904720306 +- 0.03538849577307701 diff --git a/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_H.yaml b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_H.yaml new file mode 100644 index 00000000..7c738e1e --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_H.yaml @@ -0,0 +1,88 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-23 17:02:52' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 800 +- 800 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +loss: +- 0.0010756702395156026 +- 0.0011008529691025615 +max_learning_rate: +- 0.0005000000237487257 +- 0.0005000000237487257 +max_loss: +- 4.407474517822266 +- 3.408555746078491 +max_scaled_mean_absolute_error: +- 4.813329219818115 +- 3.725175380706787 +max_scaled_root_mean_squared_error: +- 95.80032348632812 +- 75.86785125732422 +max_val_loss: +- 0.6639952659606934 +- 0.7039309740066528 +max_val_scaled_mean_absolute_error: +- 0.7249158024787903 +- 0.7690656185150146 +max_val_scaled_root_mean_squared_error: +- 0.9143601059913635 +- 0.9589051008224487 +min_learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +min_loss: +- 0.0010756702395156026 +- 0.0011008529691025615 +min_scaled_mean_absolute_error: +- 0.0011744957882910967 +- 0.0012029350036755204 +min_scaled_root_mean_squared_error: +- 0.0016492840368300676 +- 0.0016721710562705994 +min_val_loss: +- 0.008818797767162323 +- 0.009190385229885578 +min_val_scaled_mean_absolute_error: +- 0.009606427513062954 +- 0.0100205447524786 +min_val_scaled_root_mean_squared_error: +- 0.03540008142590523 +- 0.032759107649326324 +model_class: make_model +model_name: EGNN +model_version: '2023-12-04' +multi_target_indices: +- 12 +number_histories: 2 +scaled_mean_absolute_error: +- 0.0011744957882910967 +- 0.0012029350036755204 +scaled_root_mean_squared_error: +- 0.0016492840368300676 +- 0.0016721710562705994 +seed: 42 +time_list: +- '8:53:14.618473' +- '9:22:14.404742' +val_loss: +- 0.008818797767162323 +- 0.009190516546368599 +val_scaled_mean_absolute_error: +- 0.009606427513062954 +- 0.010020695626735687 +val_scaled_root_mean_squared_error: +- 0.03576206788420677 +- 0.03538968786597252 diff --git a/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_HOMO.yaml b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_HOMO.yaml new file mode 100644 index 00000000..3844c37e --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_HOMO.yaml @@ -0,0 +1,88 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-23 17:32:45' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 800 +- 800 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +loss: +- 0.0028957126196473837 +- 0.002597656799480319 +max_learning_rate: +- 0.0005000000237487257 +- 0.0005000000237487257 +max_loss: +- 3.657465934753418 +- 3.212603807449341 +max_scaled_mean_absolute_error: +- 2.2045230865478516 +- 1.9343459606170654 +max_scaled_root_mean_squared_error: +- 50.40476989746094 +- 41.87397003173828 +max_val_loss: +- 0.5486903190612793 +- 0.5359625816345215 +max_val_scaled_mean_absolute_error: +- 0.33057093620300293 +- 0.3225601315498352 +max_val_scaled_root_mean_squared_error: +- 0.452626496553421 +- 0.44354674220085144 +min_learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +min_loss: +- 0.0025854564737528563 +- 0.0024895048700273037 +min_scaled_mean_absolute_error: +- 0.0015581249026581645 +- 0.0014986685710027814 +min_scaled_root_mean_squared_error: +- 0.00232700165361166 +- 0.0021728877909481525 +min_val_loss: +- 0.050690535455942154 +- 0.04970072954893112 +min_val_scaled_mean_absolute_error: +- 0.030524469912052155 +- 0.029908040538430214 +min_val_scaled_root_mean_squared_error: +- 0.05220561474561691 +- 0.05357697978615761 +model_class: make_model +model_name: EGNN +model_version: '2023-12-04' +multi_target_indices: +- 5 +number_histories: 2 +scaled_mean_absolute_error: +- 0.0017449988517910242 +- 0.0015636744210496545 +scaled_root_mean_squared_error: +- 0.002412783447653055 +- 0.0021728877909481525 +seed: 42 +time_list: +- '9:22:51.255236' +- '9:03:34.039838' +val_loss: +- 0.05069223418831825 +- 0.04970075935125351 +val_scaled_mean_absolute_error: +- 0.030525507405400276 +- 0.029908040538430214 +val_scaled_root_mean_squared_error: +- 0.05222340673208237 +- 0.05357697978615761 diff --git a/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_LUMO.yaml b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_LUMO.yaml new file mode 100644 index 00000000..3df2f85a --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_QM9Dataset_score_LUMO.yaml @@ -0,0 +1,157 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-17 02:41:45' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 800 +- 800 +- 800 +- 800 +- 800 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +loss: +- 0.001400960492901504 +- 0.0013242654968053102 +- 0.0012470445362851024 +- 0.0012371689081192017 +- 0.0012880890863016248 +max_learning_rate: +- 0.0005000000237487257 +- 0.0005000000237487257 +- 0.0005000000237487257 +- 0.0005000000237487257 +- 0.0005000000237487257 +max_loss: +- 6.305688381195068 +- 4.809029579162598 +- 3.1519832611083984 +- 3.554156541824341 +- 3.1307685375213623 +max_scaled_mean_absolute_error: +- 8.046891212463379 +- 6.146282196044922 +- 4.030336856842041 +- 4.536959171295166 +- 4.002551078796387 +max_scaled_root_mean_squared_error: +- 193.54403686523438 +- 116.38530731201172 +- 96.53368377685547 +- 106.24433135986328 +- 88.73281860351562 +max_val_loss: +- 0.4949110448360443 +- 0.5900431871414185 +- 0.5114490389823914 +- 0.5539543628692627 +- 0.5170260071754456 +max_val_scaled_mean_absolute_error: +- 0.6313060522079468 +- 0.7538348436355591 +- 0.6536993384361267 +- 0.7069122195243835 +- 0.6607301831245422 +max_val_scaled_root_mean_squared_error: +- 0.785900890827179 +- 0.9562798142433167 +- 0.8245894312858582 +- 0.8953283429145813 +- 0.8127155303955078 +min_learning_rate: +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +- 1.927654702527093e-09 +min_loss: +- 0.0013756275875493884 +- 0.0013242654968053102 +- 0.0012460383586585522 +- 0.0012371689081192017 +- 0.001248896587640047 +min_scaled_mean_absolute_error: +- 0.0017552169738337398 +- 0.0016921148635447025 +- 0.0015930308727547526 +- 0.0015789176104590297 +- 0.0015964650083333254 +min_scaled_root_mean_squared_error: +- 0.0024910946376621723 +- 0.0023758099414408207 +- 0.0022017827723175287 +- 0.002201374154537916 +- 0.0022817845456302166 +min_val_loss: +- 0.020783184096217155 +- 0.021502533927559853 +- 0.02056189253926277 +- 0.020744536072015762 +- 0.020265070721507072 +min_val_scaled_mean_absolute_error: +- 0.026488373056054115 +- 0.027459124103188515 +- 0.02624829113483429 +- 0.026438992470502853 +- 0.025876980274915695 +min_val_scaled_root_mean_squared_error: +- 0.05178385227918625 +- 0.049761515110731125 +- 0.04848983511328697 +- 0.04962877556681633 +- 0.05374941602349281 +model_class: make_model +model_name: EGNN +model_version: '2023-12-04' +multi_target_indices: +- 6 +number_histories: 5 +scaled_mean_absolute_error: +- 0.0017874629702419043 +- 0.0016921148635447025 +- 0.0015942513709887862 +- 0.0015789176104590297 +- 0.0016464840155094862 +scaled_root_mean_squared_error: +- 0.0024910946376621723 +- 0.0023758099414408207 +- 0.0022017827723175287 +- 0.002201374154537916 +- 0.0022817845456302166 +seed: 42 +time_list: +- '8:58:54.314727' +- '9:33:56.168983' +- '9:10:01.889154' +- '9:07:10.352244' +- '9:04:33.393245' +val_loss: +- 0.02078346349298954 +- 0.021502533927559853 +- 0.020561901852488518 +- 0.020744619891047478 +- 0.020265070721507072 +val_scaled_mean_absolute_error: +- 0.026488710194826126 +- 0.027459124103188515 +- 0.02624829113483429 +- 0.02643910050392151 +- 0.025876980274915695 +val_scaled_root_mean_squared_error: +- 0.05179084092378616 +- 0.049801744520664215 +- 0.04848983511328697 +- 0.049751896411180496 +- 0.05381380394101143 diff --git a/training/results/QM9Dataset/EGNN/EGNN_hyper_G.json b/training/results/QM9Dataset/EGNN/EGNN_hyper_G.json new file mode 100644 index 00000000..6c295fd8 --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_hyper_G.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.EGNN", "config": {"name": "EGNN", "inputs": [{"shape": [null, 15], "name": "node_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "range_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128}, "input_edge_embedding": {"input_dim": 95, "output_dim": 128}, "depth": 7, "node_mlp_initialize": {"units": 128, "activation": "linear"}, "euclidean_norm_kwargs": {"keepdims": true, "axis": 1, "square_norm": true}, "use_edge_attributes": false, "edge_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "swish"]}, "edge_attention_kwargs": {"units": 1, "activation": "sigmoid"}, "use_normalized_difference": false, "expand_distance_kwargs": null, "coord_mlp_kwargs": null, "pooling_coord_kwargs": null, "pooling_edge_kwargs": {"pooling_method": "sum"}, "node_normalize_kwargs": null, "use_node_attributes": false, "node_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "use_skip": true, "verbose": 10, "node_decoder_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "node_pooling_kwargs": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "output_mlp": {"use_bias": [true, true], "units": [128, 1], "activation": ["swish", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 96, "epochs": 800, "validation_freq": 1, "verbose": 2, "callbacks": [{"class_name": "kgcnn>CosineAnnealingLRScheduler", "config": {"lr_start": 0.0005, "lr_min": 0.0, "epoch_max": 800, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularLabelScaler", "config": {}}]}}, "multi_target_indices": [13]}, "data": {}, "info": {"postfix": "", "postfix_file": "_G", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "atomic_charge_representation"}}, {"map_list": {"method": "set_range", "max_distance": 10, "max_neighbours": 10000}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/EGNN/EGNN_hyper_H.json b/training/results/QM9Dataset/EGNN/EGNN_hyper_H.json new file mode 100644 index 00000000..668154a5 --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_hyper_H.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.EGNN", "config": {"name": "EGNN", "inputs": [{"shape": [null, 15], "name": "node_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "range_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128}, "input_edge_embedding": {"input_dim": 95, "output_dim": 128}, "depth": 7, "node_mlp_initialize": {"units": 128, "activation": "linear"}, "euclidean_norm_kwargs": {"keepdims": true, "axis": 1, "square_norm": true}, "use_edge_attributes": false, "edge_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "swish"]}, "edge_attention_kwargs": {"units": 1, "activation": "sigmoid"}, "use_normalized_difference": false, "expand_distance_kwargs": null, "coord_mlp_kwargs": null, "pooling_coord_kwargs": null, "pooling_edge_kwargs": {"pooling_method": "sum"}, "node_normalize_kwargs": null, "use_node_attributes": false, "node_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "use_skip": true, "verbose": 10, "node_decoder_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "node_pooling_kwargs": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "output_mlp": {"use_bias": [true, true], "units": [128, 1], "activation": ["swish", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 96, "epochs": 800, "validation_freq": 1, "verbose": 2, "callbacks": [{"class_name": "kgcnn>CosineAnnealingLRScheduler", "config": {"lr_start": 0.0005, "lr_min": 0.0, "epoch_max": 800, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularLabelScaler", "config": {}}]}}, "multi_target_indices": [12]}, "data": {}, "info": {"postfix": "", "postfix_file": "_H", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "atomic_charge_representation"}}, {"map_list": {"method": "set_range", "max_distance": 10, "max_neighbours": 10000}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/EGNN/EGNN_hyper_HOMO.json b/training/results/QM9Dataset/EGNN/EGNN_hyper_HOMO.json new file mode 100644 index 00000000..7d2392f6 --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_hyper_HOMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.EGNN", "config": {"name": "EGNN", "inputs": [{"shape": [null, 15], "name": "node_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "range_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128}, "input_edge_embedding": {"input_dim": 95, "output_dim": 128}, "depth": 7, "node_mlp_initialize": {"units": 128, "activation": "linear"}, "euclidean_norm_kwargs": {"keepdims": true, "axis": 1, "square_norm": true}, "use_edge_attributes": false, "edge_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "swish"]}, "edge_attention_kwargs": {"units": 1, "activation": "sigmoid"}, "use_normalized_difference": false, "expand_distance_kwargs": null, "coord_mlp_kwargs": null, "pooling_coord_kwargs": null, "pooling_edge_kwargs": {"pooling_method": "sum"}, "node_normalize_kwargs": null, "use_node_attributes": false, "node_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "use_skip": true, "verbose": 10, "node_decoder_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "node_pooling_kwargs": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "output_mlp": {"use_bias": [true, true], "units": [128, 1], "activation": ["swish", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 96, "epochs": 800, "validation_freq": 1, "verbose": 2, "callbacks": [{"class_name": "kgcnn>CosineAnnealingLRScheduler", "config": {"lr_start": 0.0005, "lr_min": 0.0, "epoch_max": 800, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "StandardLabelScaler", "config": {"with_std": true, "with_mean": true, "copy": true}}]}}, "multi_target_indices": [5]}, "data": {}, "info": {"postfix": "", "postfix_file": "_HOMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "atomic_charge_representation"}}, {"map_list": {"method": "set_range", "max_distance": 10, "max_neighbours": 10000}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/EGNN/EGNN_hyper_LUMO.json b/training/results/QM9Dataset/EGNN/EGNN_hyper_LUMO.json new file mode 100644 index 00000000..eb697228 --- /dev/null +++ b/training/results/QM9Dataset/EGNN/EGNN_hyper_LUMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.EGNN", "config": {"name": "EGNN", "inputs": [{"shape": [null, 15], "name": "node_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "range_attributes", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128}, "input_edge_embedding": {"input_dim": 95, "output_dim": 128}, "depth": 7, "node_mlp_initialize": {"units": 128, "activation": "linear"}, "euclidean_norm_kwargs": {"keepdims": true, "axis": 1, "square_norm": true}, "use_edge_attributes": false, "edge_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "swish"]}, "edge_attention_kwargs": {"units": 1, "activation": "sigmoid"}, "use_normalized_difference": false, "expand_distance_kwargs": null, "coord_mlp_kwargs": null, "pooling_coord_kwargs": null, "pooling_edge_kwargs": {"pooling_method": "sum"}, "node_normalize_kwargs": null, "use_node_attributes": false, "node_mlp_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "use_skip": true, "verbose": 10, "node_decoder_kwargs": {"units": [128, 128], "activation": ["swish", "linear"]}, "node_pooling_kwargs": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "output_mlp": {"use_bias": [true, true], "units": [128, 1], "activation": ["swish", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 96, "epochs": 800, "validation_freq": 1, "verbose": 2, "callbacks": [{"class_name": "kgcnn>CosineAnnealingLRScheduler", "config": {"lr_start": 0.0005, "lr_min": 0.0, "epoch_max": 800, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "StandardLabelScaler", "config": {"with_std": true, "with_mean": true, "copy": true}}]}}, "multi_target_indices": [6]}, "data": {}, "info": {"postfix": "", "postfix_file": "_LUMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "atomic_charge_representation"}}, {"map_list": {"method": "set_range", "max_distance": 10, "max_neighbours": 10000}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_G.yaml b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_G.yaml new file mode 100644 index 00000000..fb74246b --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_G.yaml @@ -0,0 +1,229 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-14 23:22:10' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 900 +- 900 +- 900 +- 900 +- 900 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +learning_rate: +- 3.1866063636698527e-06 +- 3.1866063636698527e-06 +- 3.1866063636698527e-06 +- 3.1866063636698527e-06 +- 3.1866063636698527e-06 +loss: +- 0.004345753695815802 +- 0.002719710348173976 +- 0.004528446588665247 +- 0.004086170345544815 +- 0.003988274838775396 +max_learning_rate: +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +max_loss: +- 2715313.5 +- 2715436.25 +- 1556015.0 +- 1554059.25 +- 1555728.25 +max_mean_absolute_error: +- 2715324.75 +- 2715427.25 +- 1555954.125 +- 1554100.625 +- 1555674.625 +max_mean_squared_error: +- 8781206913024.0 +- 8780574097408.0 +- 3146053320704.0 +- 3137085898752.0 +- 3148580388864.0 +max_scaled_mean_absolute_error: +- 2715324.75 +- 2715427.25 +- 1555954.125 +- 1554100.625 +- 1555674.625 +max_scaled_root_mean_squared_error: +- 2963310.25 +- 2963203.25 +- 1773711.75 +- 1771182.0 +- 1774424.0 +max_val_loss: +- 0.25113674998283386 +- 0.12379198521375656 +- 0.1417483687400818 +- 0.07358596473932266 +- 0.11664527654647827 +max_val_mean_absolute_error: +- 0.25133195519447327 +- 0.1239221841096878 +- 0.14183872938156128 +- 0.07371462136507034 +- 0.11638732254505157 +max_val_mean_squared_error: +- 0.08244673907756805 +- 0.021617716178297997 +- 0.026244934648275375 +- 0.009878335520625114 +- 0.35234400629997253 +max_val_scaled_mean_absolute_error: +- 0.25133195519447327 +- 0.1239221841096878 +- 0.14183872938156128 +- 0.07371462136507034 +- 0.11638732254505157 +max_val_scaled_root_mean_squared_error: +- 0.28713539242744446 +- 0.14702963829040527 +- 0.16200287640094757 +- 0.09938981384038925 +- 0.593585729598999 +mean_absolute_error: +- 0.004346893634647131 +- 0.00272006937302649 +- 0.004529374651610851 +- 0.004086352419108152 +- 0.00398928951472044 +mean_squared_error: +- 3.3087133488152176e-05 +- 1.5437410183949396e-05 +- 3.46010310749989e-05 +- 2.936607415904291e-05 +- 2.8152038794360124e-05 +min_learning_rate: +- 0.0 +- 0.0 +- 0.0 +- 0.0 +- 0.0 +min_loss: +- 0.002776538720354438 +- 0.0026949101593345404 +- 0.002650545910000801 +- 0.0027271092403680086 +- 0.0026961241383105516 +min_mean_absolute_error: +- 0.0027768458239734173 +- 0.00269516883417964 +- 0.00265072425827384 +- 0.0027276126202195883 +- 0.0026965367142111063 +min_mean_squared_error: +- 1.59735900524538e-05 +- 1.5297338904929347e-05 +- 1.4839343748462852e-05 +- 1.568127663631458e-05 +- 1.4847111742710695e-05 +min_scaled_mean_absolute_error: +- 0.0027768458239734173 +- 0.00269516883417964 +- 0.00265072425827384 +- 0.0027276126202195883 +- 0.0026965367142111063 +min_scaled_root_mean_squared_error: +- 0.003996697254478931 +- 0.003911180887371302 +- 0.0038521867245435715 +- 0.003959959372878075 +- 0.0038531948812305927 +min_val_loss: +- 0.009163465350866318 +- 0.008623460307717323 +- 0.008960855193436146 +- 0.00852679181843996 +- 0.012749447487294674 +min_val_mean_absolute_error: +- 0.009077714756131172 +- 0.008611706085503101 +- 0.008767224848270416 +- 0.008513234555721283 +- 0.012540824711322784 +min_val_mean_squared_error: +- 0.0005598074058070779 +- 0.000346941378666088 +- 0.0007275175885297358 +- 0.0003557091695256531 +- 0.0039099096320569515 +min_val_scaled_mean_absolute_error: +- 0.009077714756131172 +- 0.008611706085503101 +- 0.008767224848270416 +- 0.008513234555721283 +- 0.012540824711322784 +min_val_scaled_root_mean_squared_error: +- 0.02366025000810623 +- 0.018626362085342407 +- 0.026972534134984016 +- 0.01886025257408619 +- 0.06252926588058472 +model_class: make_model +model_name: MXMNet +model_version: '2023-12-09' +multi_target_indices: +- 18 +number_histories: 5 +scaled_mean_absolute_error: +- 0.004346893634647131 +- 0.00272006937302649 +- 0.004529374651610851 +- 0.004086352419108152 +- 0.00398928951472044 +scaled_root_mean_squared_error: +- 0.005752141587436199 +- 0.003929046913981438 +- 0.005882264114916325 +- 0.005419047083705664 +- 0.005305849481374025 +seed: 42 +time_list: +- 1 day, 4:38:16.487069 +- 1 day, 4:28:34.032438 +- 1 day, 3:40:04.158962 +- 1 day, 4:46:24.362303 +- 1 day, 4:30:36.389094 +val_loss: +- 0.009229273535311222 +- 0.009327396750450134 +- 0.009426582604646683 +- 0.01068523246794939 +- 0.012788796797394753 +val_mean_absolute_error: +- 0.009142301045358181 +- 0.009319420903921127 +- 0.009234023280441761 +- 0.010667761787772179 +- 0.012580754235386848 +val_mean_squared_error: +- 0.0009260413353331387 +- 0.0003675629268400371 +- 0.0007416953449137509 +- 0.0004044637316837907 +- 0.35234400629997253 +val_scaled_mean_absolute_error: +- 0.009142301045358181 +- 0.009319420903921127 +- 0.009234023280441761 +- 0.010667761787772179 +- 0.012580754235386848 +val_scaled_root_mean_squared_error: +- 0.030430926010012627 +- 0.019171930849552155 +- 0.02723408304154873 +- 0.020111283287405968 +- 0.593585729598999 diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_H.yaml b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_H.yaml new file mode 100644 index 00000000..f620f5a2 --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_H.yaml @@ -0,0 +1,124 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-24 14:07:25' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 900 +- 900 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 3.1866063636698527e-06 +- 3.1866063636698527e-06 +loss: +- 0.002213386818766594 +- 0.0026501708198338747 +max_learning_rate: +- 9.999999747378752e-05 +- 9.999999747378752e-05 +max_loss: +- 1554053.125 +- 1555721.75 +max_mean_absolute_error: +- 1554094.625 +- 1555668.125 +max_mean_squared_error: +- 3137068072960.0 +- 3148559941632.0 +max_scaled_mean_absolute_error: +- 1554094.625 +- 1555668.125 +max_scaled_root_mean_squared_error: +- 1771177.0 +- 1774418.25 +max_val_loss: +- 0.2166866958141327 +- 0.09389010071754456 +max_val_mean_absolute_error: +- 0.21607866883277893 +- 0.09375178813934326 +max_val_mean_squared_error: +- 0.056355491280555725 +- 1.902894377708435 +max_val_scaled_mean_absolute_error: +- 0.21607866883277893 +- 0.09375178813934326 +max_val_scaled_root_mean_squared_error: +- 0.2373931109905243 +- 1.3794543743133545 +mean_absolute_error: +- 0.0022136522457003593 +- 0.0026506620924919844 +mean_squared_error: +- 9.020595825859345e-06 +- 1.2643344234675169e-05 +min_learning_rate: +- 0.0 +- 0.0 +min_loss: +- 0.002213386818766594 +- 0.0022141484078019857 +min_mean_absolute_error: +- 0.0022136522457003593 +- 0.002214109757915139 +min_mean_squared_error: +- 9.020595825859345e-06 +- 8.868021723174024e-06 +min_scaled_mean_absolute_error: +- 0.0022136522457003593 +- 0.002214109757915139 +min_scaled_root_mean_squared_error: +- 0.003003430785611272 +- 0.002977922325953841 +min_val_loss: +- 0.00743128964677453 +- 0.016484372317790985 +min_val_mean_absolute_error: +- 0.0074112010188400745 +- 0.01621674932539463 +min_val_mean_squared_error: +- 0.0003903789329342544 +- 0.011060231365263462 +min_val_scaled_mean_absolute_error: +- 0.0074112010188400745 +- 0.01621674932539463 +min_val_scaled_root_mean_squared_error: +- 0.019758008420467377 +- 0.10516763478517532 +model_class: make_model +model_name: MXMNet +model_version: '2023-12-09' +multi_target_indices: +- 17 +number_histories: 2 +scaled_mean_absolute_error: +- 0.0022136522457003593 +- 0.0026506620924919844 +scaled_root_mean_squared_error: +- 0.003003430785611272 +- 0.003555747913196683 +seed: 42 +time_list: +- 1 day, 4:46:25.801978 +- 1 day, 4:29:22.767528 +val_loss: +- 0.00743128964677453 +- 0.016571108251810074 +val_mean_absolute_error: +- 0.0074112010188400745 +- 0.016321230679750443 +val_mean_squared_error: +- 0.0003907398786395788 +- 1.902894377708435 +val_scaled_mean_absolute_error: +- 0.0074112010188400745 +- 0.016321230679750443 +val_scaled_root_mean_squared_error: +- 0.019767140969634056 +- 1.3794543743133545 diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_HOMO.yaml b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_HOMO.yaml new file mode 100644 index 00000000..5eab1a04 --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_HOMO.yaml @@ -0,0 +1,124 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-24 13:11:12' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 900 +- 900 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 3.186606409144588e-05 +- 3.186606409144588e-05 +loss: +- 0.01078067161142826 +- 0.00478322384878993 +max_learning_rate: +- 0.0010000000474974513 +- 0.0010000000474974513 +max_loss: +- 3608025.25 +- 3608167.0 +max_mean_absolute_error: +- 3608055.0 +- 3608097.75 +max_mean_squared_error: +- 16038961872896.0 +- 16010225647616.0 +max_scaled_mean_absolute_error: +- 3608055.0 +- 3608097.75 +max_scaled_root_mean_squared_error: +- 4004867.25 +- 4001278.0 +max_val_loss: +- 0.11132214218378067 +- 0.24343882501125336 +max_val_mean_absolute_error: +- 0.11099009960889816 +- 0.24326354265213013 +max_val_mean_squared_error: +- 0.020909026265144348 +- 1.187813639640808 +max_val_scaled_mean_absolute_error: +- 0.11099009960889816 +- 0.24326354265213013 +max_val_scaled_root_mean_squared_error: +- 0.1445995420217514 +- 1.0898686647415161 +mean_absolute_error: +- 0.010778913274407387 +- 0.004784419666975737 +mean_squared_error: +- 0.0001861106138676405 +- 4.498466296354309e-05 +min_learning_rate: +- 0.0 +- 0.0 +min_loss: +- 0.008195515722036362 +- 0.003713286481797695 +min_mean_absolute_error: +- 0.008196063339710236 +- 0.00371303828433156 +min_mean_squared_error: +- 0.0001453463191865012 +- 2.6165618692175485e-05 +min_scaled_mean_absolute_error: +- 0.008196063339710236 +- 0.00371303828433156 +min_scaled_root_mean_squared_error: +- 0.012055966071784496 +- 0.005115233827382326 +min_val_loss: +- 0.031849466264247894 +- 0.031604230403900146 +min_val_mean_absolute_error: +- 0.03171669691801071 +- 0.031424228101968765 +min_val_mean_squared_error: +- 0.0022724990267306566 +- 0.00259683420881629 +min_val_scaled_mean_absolute_error: +- 0.03171669691801071 +- 0.031424228101968765 +min_val_scaled_root_mean_squared_error: +- 0.04767073318362236 +- 0.05095914006233215 +model_class: make_model +model_name: MXMNet +model_version: '2023-12-09' +multi_target_indices: +- 5 +number_histories: 2 +scaled_mean_absolute_error: +- 0.010778913274407387 +- 0.004784419666975737 +scaled_root_mean_squared_error: +- 0.013642235659062862 +- 0.006707060616463423 +seed: 42 +time_list: +- 1 day, 4:56:14.212757 +- 1 day, 4:47:31.396423 +val_loss: +- 0.036266177892684937 +- 0.03359147906303406 +val_mean_absolute_error: +- 0.036107782274484634 +- 0.03340762108564377 +val_mean_squared_error: +- 0.0028413906693458557 +- 0.002803423907607794 +val_scaled_mean_absolute_error: +- 0.036107782274484634 +- 0.03340762108564377 +val_scaled_root_mean_squared_error: +- 0.05330469459295273 +- 0.05294736847281456 diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_LUMO.yaml b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_LUMO.yaml new file mode 100644 index 00000000..e96e02cf --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_QM9Dataset_score_LUMO.yaml @@ -0,0 +1,229 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-15 00:35:16' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 900 +- 900 +- 900 +- 900 +- 900 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +learning_rate: +- 3.186606409144588e-05 +- 3.186606409144588e-05 +- 3.186606409144588e-05 +- 3.186606409144588e-05 +- 3.186606409144588e-05 +loss: +- 0.0052193463779985905 +- 0.005540700163692236 +- 0.005060866940766573 +- 0.006349912844598293 +- 0.002912594471126795 +max_learning_rate: +- 0.0010000000474974513 +- 0.0010000000474974513 +- 0.0010000000474974513 +- 0.0010000000474974513 +- 0.0010000000474974513 +max_loss: +- 3273582.25 +- 3275527.5 +- 3608754.0 +- 3608031.25 +- 3608172.0 +max_mean_absolute_error: +- 3273611.25 +- 3275520.25 +- 3608642.75 +- 3608061.0 +- 3608102.75 +max_mean_squared_error: +- 12146702286848.0 +- 12515201253376.0 +- 15891367460864.0 +- 16039011155968.0 +- 16010270736384.0 +max_scaled_mean_absolute_error: +- 3273611.25 +- 3275520.25 +- 3608642.75 +- 3608061.0 +- 3608102.75 +max_scaled_root_mean_squared_error: +- 3485212.0 +- 3537683.0 +- 3986397.75 +- 4004873.5 +- 4001283.5 +max_val_loss: +- 0.1242666095495224 +- 0.1026378944516182 +- 107.91532135009766 +- 0.09408698976039886 +- 0.08499129116535187 +max_val_mean_absolute_error: +- 0.12414339929819107 +- 0.10223794728517532 +- 108.23210144042969 +- 0.09400157630443573 +- 0.08473872393369675 +max_val_mean_squared_error: +- 0.026183540001511574 +- 0.017892230302095413 +- 304373920.0 +- 0.015151355415582657 +- 0.014569687657058239 +max_val_scaled_mean_absolute_error: +- 0.12414339929819107 +- 0.10223794728517532 +- 108.23210144042969 +- 0.09400157630443573 +- 0.08473872393369675 +max_val_scaled_root_mean_squared_error: +- 0.16181328892707825 +- 0.13376183807849884 +- 17446.314453125 +- 0.12309084087610245 +- 0.12070495635271072 +mean_absolute_error: +- 0.005220427177846432 +- 0.005542372353374958 +- 0.0050599416717886925 +- 0.006351281423121691 +- 0.0029129376634955406 +mean_squared_error: +- 4.645034277928062e-05 +- 5.360971044865437e-05 +- 5.102927752886899e-05 +- 7.391578401438892e-05 +- 1.5837085811654106e-05 +min_learning_rate: +- 0.0 +- 0.0 +- 0.0 +- 0.0 +- 0.0 +min_loss: +- 0.0028165995609015226 +- 0.0031651463359594345 +- 0.003249892732128501 +- 0.0027931167278438807 +- 0.0028115762397646904 +min_mean_absolute_error: +- 0.0028170295991003513 +- 0.0031658196821808815 +- 0.0032503949478268623 +- 0.0027934941463172436 +- 0.0028120791539549828 +min_mean_squared_error: +- 1.5038216588436626e-05 +- 1.9220087779103778e-05 +- 1.8766089851851575e-05 +- 1.459224586142227e-05 +- 1.5723555407021195e-05 +min_scaled_mean_absolute_error: +- 0.0028170295991003513 +- 0.0031658196821808815 +- 0.0032503949478268623 +- 0.0027934941463172436 +- 0.0028120791539549828 +min_scaled_root_mean_squared_error: +- 0.003877913812175393 +- 0.004384072031825781 +- 0.004331984557211399 +- 0.003819979727268219 +- 0.003965293988585472 +min_val_loss: +- 0.02634292282164097 +- 0.027860181406140327 +- 0.026609383523464203 +- 0.02638959139585495 +- 0.02816171385347843 +min_val_mean_absolute_error: +- 0.026237202808260918 +- 0.027711372822523117 +- 0.02641715481877327 +- 0.026300376281142235 +- 0.027768772095441818 +min_val_mean_squared_error: +- 0.0025622823741286993 +- 0.0021408076863735914 +- 0.002323558321222663 +- 0.0019799498841166496 +- 0.002490648068487644 +min_val_scaled_mean_absolute_error: +- 0.026237202808260918 +- 0.027711372822523117 +- 0.02641715481877327 +- 0.026300376281142235 +- 0.027768772095441818 +min_val_scaled_root_mean_squared_error: +- 0.05061899125576019 +- 0.04626886174082756 +- 0.048203300684690475 +- 0.044496629387140274 +- 0.04990639165043831 +model_class: make_model +model_name: MXMNet +model_version: '2023-12-09' +multi_target_indices: +- 6 +number_histories: 5 +scaled_mean_absolute_error: +- 0.005220427177846432 +- 0.005542372353374958 +- 0.0050599416717886925 +- 0.006351281423121691 +- 0.0029129376634955406 +scaled_root_mean_squared_error: +- 0.006815448869019747 +- 0.007321864832192659 +- 0.007143477909266949 +- 0.008597428910434246 +- 0.003979583270847797 +seed: 42 +time_list: +- 1 day, 4:57:01.945405 +- 1 day, 4:12:28.734157 +- 1 day, 6:00:57.666134 +- 1 day, 4:05:02.262043 +- 1 day, 5:43:43.753431 +val_loss: +- 0.027214525267481804 +- 0.027860181406140327 +- 0.0305512435734272 +- 0.03316011652350426 +- 0.028273137286305428 +val_mean_absolute_error: +- 0.027112141251564026 +- 0.027711372822523117 +- 0.030369894579052925 +- 0.033062126487493515 +- 0.027925482019782066 +val_mean_squared_error: +- 0.002728046616539359 +- 0.002145886654034257 +- 0.002655827207490802 +- 0.0024532335810363293 +- 0.0025647510774433613 +val_scaled_mean_absolute_error: +- 0.027112141251564026 +- 0.027711372822523117 +- 0.030369894579052925 +- 0.033062126487493515 +- 0.027925482019782066 +val_scaled_root_mean_squared_error: +- 0.052230704575777054 +- 0.04632371664047241 +- 0.05153471603989601 +- 0.04953012615442276 +- 0.05064336955547333 diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_hyper_G.json b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_G.json new file mode 100644 index 00000000..0e4dea8c --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_G.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.MXMNet", "config": {"name": "MXMNet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "edge_weights", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_1", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_2", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "input_edge_embedding": {"input_dim": 32, "output_dim": 128}, "bessel_basis_local": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5}, "bessel_basis_global": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5}, "spherical_basis_local": {"num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5}, "mlp_rbf_kwargs": {"units": 128, "activation": "swish"}, "mlp_sbf_kwargs": {"units": 128, "activation": "swish"}, "global_mp_kwargs": {"units": 128}, "local_mp_kwargs": {"units": 128, "output_units": 1, "output_kernel_initializer": "glorot_uniform"}, "use_edge_attributes": false, "depth": 6, "verbose": 10, "node_pooling_args": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "use_output_mlp": false, "output_mlp": {"use_bias": [true], "units": [1], "activation": ["linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 900, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearWarmupExponentialLRScheduler", "config": {"lr_start": 0.0001, "gamma": 0.9961697, "epo_warmup": 1, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001, "global_clipnorm": 1000}}, "loss": "mean_absolute_error", "metrics": ["mean_absolute_error", "mean_squared_error", {"class_name": "RootMeanSquaredError", "config": {"name": "scaled_root_mean_squared_error"}}, {"class_name": "MeanAbsoluteError", "config": {"name": "scaled_mean_absolute_error"}}]}, "multi_target_indices": [18]}, "data": {}, "info": {"postfix": "", "postfix_file": "_G", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"remove_uncharacterized": {}}, {"map_list": {"method": "set_edge_weights_uniform"}}, {"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "jk", "angle_indices": "angle_indices_1", "angle_indices_nodes": "angle_indices_nodes_1", "angle_attributes": "angle_attributes_1"}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "ik", "allow_self_edges": true, "angle_indices": "angle_indices_2", "angle_indices_nodes": "angle_indices_nodes_2", "angle_attributes": "angle_attributes_2"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_hyper_H.json b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_H.json new file mode 100644 index 00000000..43700f09 --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_H.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.MXMNet", "config": {"name": "MXMNet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "edge_weights", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_1", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_2", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "input_edge_embedding": {"input_dim": 32, "output_dim": 128}, "bessel_basis_local": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5}, "bessel_basis_global": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5}, "spherical_basis_local": {"num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5}, "mlp_rbf_kwargs": {"units": 128, "activation": "swish"}, "mlp_sbf_kwargs": {"units": 128, "activation": "swish"}, "global_mp_kwargs": {"units": 128}, "local_mp_kwargs": {"units": 128, "output_units": 1, "output_kernel_initializer": "glorot_uniform"}, "use_edge_attributes": false, "depth": 6, "verbose": 10, "node_pooling_args": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "use_output_mlp": false, "output_mlp": {"use_bias": [true], "units": [1], "activation": ["linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 900, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearWarmupExponentialLRScheduler", "config": {"lr_start": 0.0001, "gamma": 0.9961697, "epo_warmup": 1, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001, "global_clipnorm": 1000}}, "loss": "mean_absolute_error", "metrics": ["mean_absolute_error", "mean_squared_error", {"class_name": "RootMeanSquaredError", "config": {"name": "scaled_root_mean_squared_error"}}, {"class_name": "MeanAbsoluteError", "config": {"name": "scaled_mean_absolute_error"}}]}, "multi_target_indices": [17]}, "data": {}, "info": {"postfix": "", "postfix_file": "_H", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"remove_uncharacterized": {}}, {"map_list": {"method": "set_edge_weights_uniform"}}, {"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "jk", "angle_indices": "angle_indices_1", "angle_indices_nodes": "angle_indices_nodes_1", "angle_attributes": "angle_attributes_1"}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "ik", "allow_self_edges": true, "angle_indices": "angle_indices_2", "angle_indices_nodes": "angle_indices_nodes_2", "angle_attributes": "angle_attributes_2"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_hyper_HOMO.json b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_HOMO.json new file mode 100644 index 00000000..55d1ee65 --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_HOMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.MXMNet", "config": {"name": "MXMNet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "edge_weights", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_1", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_2", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "input_edge_embedding": {"input_dim": 32, "output_dim": 128}, "bessel_basis_local": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5}, "bessel_basis_global": {"num_radial": 16, "cutoff": 10.0, "envelope_exponent": 5}, "spherical_basis_local": {"num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5}, "mlp_rbf_kwargs": {"units": 128, "activation": "swish"}, "mlp_sbf_kwargs": {"units": 128, "activation": "swish"}, "global_mp_kwargs": {"units": 128}, "local_mp_kwargs": {"units": 128, "output_units": 1, "output_kernel_initializer": "glorot_uniform"}, "use_edge_attributes": false, "depth": 6, "verbose": 10, "node_pooling_args": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "use_output_mlp": false, "output_mlp": {"use_bias": [true], "units": [1], "activation": ["linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 900, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearWarmupExponentialLRScheduler", "config": {"lr_start": 0.001, "gamma": 0.9961697, "epo_warmup": 1, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001, "global_clipnorm": 1000}}, "loss": "mean_absolute_error", "metrics": ["mean_absolute_error", "mean_squared_error", {"class_name": "RootMeanSquaredError", "config": {"name": "scaled_root_mean_squared_error"}}, {"class_name": "MeanAbsoluteError", "config": {"name": "scaled_mean_absolute_error"}}]}, "multi_target_indices": [5]}, "data": {}, "info": {"postfix": "", "postfix_file": "_HOMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"remove_uncharacterized": {}}, {"map_list": {"method": "set_edge_weights_uniform"}}, {"map_list": {"method": "set_range", "max_distance": 10, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "jk", "angle_indices": "angle_indices_1", "angle_indices_nodes": "angle_indices_nodes_1", "angle_attributes": "angle_attributes_1"}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "ik", "allow_self_edges": true, "angle_indices": "angle_indices_2", "angle_indices_nodes": "angle_indices_nodes_2", "angle_attributes": "angle_attributes_2"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/MXMNet/MXMNet_hyper_LUMO.json b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_LUMO.json new file mode 100644 index 00000000..8268ac36 --- /dev/null +++ b/training/results/QM9Dataset/MXMNet/MXMNet_hyper_LUMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.MXMNet", "config": {"name": "MXMNet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 1], "name": "edge_weights", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_1", "dtype": "int64", "ragged": true}, {"shape": [null, 2], "name": "angle_indices_2", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 128, "embeddings_initializer": {"class_name": "RandomUniform", "config": {"minval": -1.7320508075688772, "maxval": 1.7320508075688772}}}, "input_edge_embedding": {"input_dim": 32, "output_dim": 128}, "bessel_basis_local": {"num_radial": 16, "cutoff": 5.0, "envelope_exponent": 5}, "bessel_basis_global": {"num_radial": 16, "cutoff": 10.0, "envelope_exponent": 5}, "spherical_basis_local": {"num_spherical": 7, "num_radial": 6, "cutoff": 5.0, "envelope_exponent": 5}, "mlp_rbf_kwargs": {"units": 128, "activation": "swish"}, "mlp_sbf_kwargs": {"units": 128, "activation": "swish"}, "global_mp_kwargs": {"units": 128}, "local_mp_kwargs": {"units": 128, "output_units": 1, "output_kernel_initializer": "glorot_uniform"}, "use_edge_attributes": false, "depth": 6, "verbose": 10, "node_pooling_args": {"pooling_method": "sum"}, "output_embedding": "graph", "output_to_tensor": true, "use_output_mlp": false, "output_mlp": {"use_bias": [true], "units": [1], "activation": ["linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 900, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearWarmupExponentialLRScheduler", "config": {"lr_start": 0.001, "gamma": 0.9961697, "epo_warmup": 1, "verbose": 1}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001, "global_clipnorm": 1000}}, "loss": "mean_absolute_error", "metrics": ["mean_absolute_error", "mean_squared_error", {"class_name": "RootMeanSquaredError", "config": {"name": "scaled_root_mean_squared_error"}}, {"class_name": "MeanAbsoluteError", "config": {"name": "scaled_mean_absolute_error"}}]}, "multi_target_indices": [6]}, "data": {}, "info": {"postfix": "", "postfix_file": "_LUMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"remove_uncharacterized": {}}, {"map_list": {"method": "set_edge_weights_uniform"}}, {"map_list": {"method": "set_range", "max_distance": 10, "max_neighbours": 1000}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "jk", "angle_indices": "angle_indices_1", "angle_indices_nodes": "angle_indices_nodes_1", "angle_attributes": "angle_attributes_1"}}, {"map_list": {"method": "set_angle", "range_indices": "edge_indices", "edge_pairing": "ik", "allow_self_edges": true, "angle_indices": "angle_indices_2", "angle_indices_nodes": "angle_indices_nodes_2", "angle_attributes": "angle_attributes_2"}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_H.yaml b/training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_H.yaml new file mode 100644 index 00000000..f7a32500 --- /dev/null +++ b/training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_H.yaml @@ -0,0 +1,88 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-24 21:09:56' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 800 +- 800 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 1.06999996205559e-05 +- 1.06999996205559e-05 +loss: +- 0.004764853976666927 +- 0.004947832319885492 +max_learning_rate: +- 0.0005000000237487257 +- 0.0005000000237487257 +max_loss: +- 0.7773099541664124 +- 0.7771309018135071 +max_scaled_mean_absolute_error: +- 0.8486399054527283 +- 0.8490580916404724 +max_scaled_root_mean_squared_error: +- 1.0785257816314697 +- 1.077731966972351 +max_val_loss: +- 0.10990142077207565 +- 0.11022701114416122 +max_val_scaled_mean_absolute_error: +- 0.11967755109071732 +- 0.12017706781625748 +max_val_scaled_root_mean_squared_error: +- 0.19413302838802338 +- 0.2035401165485382 +min_learning_rate: +- 1.06999996205559e-05 +- 1.06999996205559e-05 +min_loss: +- 0.004764853976666927 +- 0.004947832319885492 +min_scaled_mean_absolute_error: +- 0.005201948340982199 +- 0.005405828822404146 +min_scaled_root_mean_squared_error: +- 0.007367674261331558 +- 0.007595034781843424 +min_val_loss: +- 0.014941912144422531 +- 0.015913689509034157 +min_val_scaled_mean_absolute_error: +- 0.016232075169682503 +- 0.017248570919036865 +min_val_scaled_root_mean_squared_error: +- 0.037356771528720856 +- 0.04759074002504349 +model_class: make_model +model_name: Megnet +model_version: '2023-12-05' +multi_target_indices: +- 12 +number_histories: 2 +scaled_mean_absolute_error: +- 0.005201948340982199 +- 0.005405828822404146 +scaled_root_mean_squared_error: +- 0.007367674261331558 +- 0.007595034781843424 +seed: 42 +time_list: +- 1 day, 3:27:14.314629 +- 1 day, 3:02:53.012669 +val_loss: +- 0.015188874676823616 +- 0.015913689509034157 +val_scaled_mean_absolute_error: +- 0.016505751758813858 +- 0.017248570919036865 +val_scaled_root_mean_squared_error: +- 0.03748267516493797 +- 0.051103245466947556 diff --git a/training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_HOMO.yaml b/training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_HOMO.yaml new file mode 100644 index 00000000..6c427f41 --- /dev/null +++ b/training/results/QM9Dataset/Megnet/Megnet_QM9Dataset_score_HOMO.yaml @@ -0,0 +1,88 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-24 08:48:20' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 800 +- 800 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 1.06999996205559e-05 +- 1.06999996205559e-05 +loss: +- 0.015953239053487778 +- 0.016383972018957138 +max_learning_rate: +- 0.0005000000237487257 +- 0.0005000000237487257 +max_loss: +- 0.571775496006012 +- 0.5746840238571167 +max_scaled_mean_absolute_error: +- 0.34452834725379944 +- 0.34592726826667786 +max_scaled_root_mean_squared_error: +- 0.48018452525138855 +- 0.48139113187789917 +max_val_loss: +- 0.1906891018152237 +- 0.20106293261051178 +max_val_scaled_mean_absolute_error: +- 0.11481630057096481 +- 0.12096023559570312 +max_val_scaled_root_mean_squared_error: +- 0.16864857077598572 +- 0.17896375060081482 +min_learning_rate: +- 1.06999996205559e-05 +- 1.06999996205559e-05 +min_loss: +- 0.015802957117557526 +- 0.016289122402668 +min_scaled_mean_absolute_error: +- 0.009522140957415104 +- 0.009805066511034966 +min_scaled_root_mean_squared_error: +- 0.014821133576333523 +- 0.017847513779997826 +min_val_loss: +- 0.0758657306432724 +- 0.07961893826723099 +min_val_scaled_mean_absolute_error: +- 0.0456601046025753 +- 0.047898560762405396 +min_val_scaled_root_mean_squared_error: +- 0.06968275457620621 +- 0.07626539468765259 +model_class: make_model +model_name: Megnet +model_version: '2023-12-05' +multi_target_indices: +- 5 +number_histories: 2 +scaled_mean_absolute_error: +- 0.009612714871764183 +- 0.009862092323601246 +scaled_root_mean_squared_error: +- 0.014880934730172157 +- 0.017847513779997826 +seed: 42 +time_list: +- 1 day, 0:39:02.240716 +- 1 day, 3:19:02.522863 +val_loss: +- 0.0758657306432724 +- 0.07961893826723099 +val_scaled_mean_absolute_error: +- 0.0456601046025753 +- 0.047898560762405396 +val_scaled_root_mean_squared_error: +- 0.07025036215782166 +- 0.07659842818975449 diff --git a/training/results/QM9Dataset/Megnet/Megnet_hyper_H.json b/training/results/QM9Dataset/Megnet/Megnet_hyper_H.json new file mode 100644 index 00000000..67dd9bd2 --- /dev/null +++ b/training/results/QM9Dataset/Megnet/Megnet_hyper_H.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.Megnet", "config": {"name": "Megnet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [2], "name": "graph_attributes", "dtype": "float32", "ragged": false}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 10, "output_dim": 16}, "gauss_args": {"bins": 20, "distance": 4, "offset": 0.0, "sigma": 0.4}, "meg_block_args": {"node_embed": [64, 32, 32], "edge_embed": [64, 32, 32], "env_embed": [64, 32, 32], "activation": "kgcnn>softplus2"}, "set2set_args": {"channels": 16, "T": 3, "pooling_method": "sum", "init_qstar": "0"}, "node_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "edge_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "state_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "nblocks": 3, "has_ff": true, "dropout": null, "use_set2set": true, "verbose": 10, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true, true], "units": [32, 16, 1], "activation": ["kgcnn>softplus2", "kgcnn>softplus2", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 800, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0005, "learning_rate_stop": 1e-05, "epo_min": 100, "epo": 800, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularLabelScaler", "config": {}}]}}, "multi_target_indices": [12]}, "data": {}, "info": {"postfix": "", "postfix_file": "_H", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 30}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/Megnet/Megnet_hyper_HOMO.json b/training/results/QM9Dataset/Megnet/Megnet_hyper_HOMO.json new file mode 100644 index 00000000..64eac7f6 --- /dev/null +++ b/training/results/QM9Dataset/Megnet/Megnet_hyper_HOMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.Megnet", "config": {"name": "Megnet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [2], "name": "graph_attributes", "dtype": "float32", "ragged": false}], "input_embedding": null, "input_tensor_type": "ragged", "input_node_embedding": {"input_dim": 10, "output_dim": 16}, "gauss_args": {"bins": 20, "distance": 4, "offset": 0.0, "sigma": 0.4}, "meg_block_args": {"node_embed": [64, 32, 32], "edge_embed": [64, 32, 32], "env_embed": [64, 32, 32], "activation": "kgcnn>softplus2"}, "set2set_args": {"channels": 16, "T": 3, "pooling_method": "sum", "init_qstar": "0"}, "node_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "edge_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "state_ff_args": {"units": [64, 32], "activation": "kgcnn>softplus2"}, "nblocks": 3, "has_ff": true, "dropout": null, "use_set2set": true, "verbose": 10, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true, true], "units": [32, 16, 1], "activation": ["kgcnn>softplus2", "kgcnn>softplus2", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 800, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0005, "learning_rate_stop": 1e-05, "epo_min": 100, "epo": 800, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "StandardLabelScaler", "config": {"with_std": true, "with_mean": true, "copy": true}}]}}, "multi_target_indices": [5]}, "data": {}, "info": {"postfix": "", "postfix_file": "_HOMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 30}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_G.yaml b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_G.yaml new file mode 100644 index 00000000..5456444e --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_G.yaml @@ -0,0 +1,157 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-14 08:57:37' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 700 +- 700 +- 700 +- 700 +- 700 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +loss: +- 0.010881002992391586 +- 0.009958148002624512 +- 0.010110955685377121 +- 0.01070237997919321 +- 0.00987672433257103 +max_learning_rate: +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +max_loss: +- 0.4885646402835846 +- 0.4938715100288391 +- 0.48627087473869324 +- 0.4781111180782318 +- 0.47534143924713135 +max_scaled_mean_absolute_error: +- 0.5361679196357727 +- 0.5428919792175293 +- 0.5343784093856812 +- 0.5256630778312683 +- 0.5228813886642456 +max_scaled_root_mean_squared_error: +- 0.6992903351783752 +- 0.7032381296157837 +- 0.6966925859451294 +- 0.6919498443603516 +- 0.6882494688034058 +max_val_loss: +- 0.10586613416671753 +- 0.10517912358045578 +- 0.10861948132514954 +- 0.11656937003135681 +- 0.10176766663789749 +max_val_scaled_mean_absolute_error: +- 0.1159529834985733 +- 0.11545488983392715 +- 0.11912660300731659 +- 0.12798912823200226 +- 0.11176032572984695 +max_val_scaled_root_mean_squared_error: +- 0.1713990718126297 +- 0.17182421684265137 +- 0.18038778007030487 +- 0.18806535005569458 +- 0.16963551938533783 +min_learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +min_loss: +- 0.010881002992391586 +- 0.009958148002624512 +- 0.010077420622110367 +- 0.010656449943780899 +- 0.00987672433257103 +min_scaled_mean_absolute_error: +- 0.011941027827560902 +- 0.01094652060419321 +- 0.011074223555624485 +- 0.011716015636920929 +- 0.01086464524269104 +min_scaled_root_mean_squared_error: +- 0.02050674892961979 +- 0.01784401759505272 +- 0.01835654303431511 +- 0.01857464574277401 +- 0.01984807476401329 +min_val_loss: +- 0.04901701584458351 +- 0.0465129055082798 +- 0.04913158342242241 +- 0.046940892934799194 +- 0.047800835222005844 +min_val_scaled_mean_absolute_error: +- 0.05359926074743271 +- 0.050960857421159744 +- 0.05376289039850235 +- 0.05148845911026001 +- 0.0524408221244812 +min_val_scaled_root_mean_squared_error: +- 0.09045984596014023 +- 0.08648891746997833 +- 0.0950661227107048 +- 0.08700945973396301 +- 0.08836081624031067 +model_class: make_model +model_name: NMPN +model_version: '2023-11-22' +multi_target_indices: +- 13 +number_histories: 5 +scaled_mean_absolute_error: +- 0.011941027827560902 +- 0.01094652060419321 +- 0.011111132800579071 +- 0.011766530573368073 +- 0.01086464524269104 +scaled_root_mean_squared_error: +- 0.02050674892961979 +- 0.01784401759505272 +- 0.018378468230366707 +- 0.018811725080013275 +- 0.01984807476401329 +seed: 42 +time_list: +- '14:37:42.382429' +- '14:44:19.067360' +- '14:13:46.547611' +- '14:12:58.069710' +- '14:12:01.557404' +val_loss: +- 0.04904879257082939 +- 0.0466703400015831 +- 0.04913158342242241 +- 0.04754279553890228 +- 0.048583026975393295 +val_scaled_mean_absolute_error: +- 0.053627073764801025 +- 0.05113530158996582 +- 0.05376289039850235 +- 0.05214400961995125 +- 0.05329621210694313 +val_scaled_root_mean_squared_error: +- 0.09095483273267746 +- 0.0867360457777977 +- 0.0950661227107048 +- 0.08805331587791443 +- 0.08992594480514526 diff --git a/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_H.yaml b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_H.yaml new file mode 100644 index 00000000..92855598 --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_H.yaml @@ -0,0 +1,88 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-24 08:08:03' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 700 +- 700 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +loss: +- 0.011429122649133205 +- 0.010737859643995762 +max_learning_rate: +- 9.999999747378752e-05 +- 9.999999747378752e-05 +max_loss: +- 0.5030210018157959 +- 0.4944944977760315 +max_scaled_mean_absolute_error: +- 0.5491960048675537 +- 0.5402727723121643 +max_scaled_root_mean_squared_error: +- 0.7163705229759216 +- 0.7079967856407166 +max_val_loss: +- 0.11011811345815659 +- 0.10715076327323914 +max_val_scaled_mean_absolute_error: +- 0.12001536786556244 +- 0.11688880622386932 +max_val_scaled_root_mean_squared_error: +- 0.18209540843963623 +- 0.17592087388038635 +min_learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +min_loss: +- 0.011429122649133205 +- 0.010676178149878979 +min_scaled_mean_absolute_error: +- 0.012477696873247623 +- 0.011664663441479206 +min_scaled_root_mean_squared_error: +- 0.024382978677749634 +- 0.022128688171505928 +min_val_loss: +- 0.04856857657432556 +- 0.0511004775762558 +min_val_scaled_mean_absolute_error: +- 0.05282432585954666 +- 0.05567791312932968 +min_val_scaled_root_mean_squared_error: +- 0.09097553789615631 +- 0.0949467346072197 +model_class: make_model +model_name: NMPN +model_version: '2023-11-22' +multi_target_indices: +- 12 +number_histories: 2 +scaled_mean_absolute_error: +- 0.012477696873247623 +- 0.011732123792171478 +scaled_root_mean_squared_error: +- 0.0244058296084404 +- 0.022206485271453857 +seed: 42 +time_list: +- '14:40:24.697245' +- '14:40:53.357599' +val_loss: +- 0.049180060625076294 +- 0.05113501846790314 +val_scaled_mean_absolute_error: +- 0.05348145216703415 +- 0.05570865049958229 +val_scaled_root_mean_squared_error: +- 0.09295357763767242 +- 0.09528426826000214 diff --git a/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_HOMO.yaml b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_HOMO.yaml new file mode 100644 index 00000000..bd78e232 --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_HOMO.yaml @@ -0,0 +1,88 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-23 22:09:54' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 700 +- 700 +execute_folds: +- 3 +kgcnn_version: 4.0.1 +learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +loss: +- 0.02223457396030426 +- 0.020672369748353958 +max_learning_rate: +- 9.999999747378752e-05 +- 9.999999747378752e-05 +max_loss: +- 0.4959501326084137 +- 0.4994843304157257 +max_scaled_mean_absolute_error: +- 0.29884040355682373 +- 0.30065858364105225 +max_scaled_root_mean_squared_error: +- 0.4097099304199219 +- 0.4100411832332611 +max_val_loss: +- 0.18062184751033783 +- 0.17841483652591705 +max_val_scaled_mean_absolute_error: +- 0.10875657200813293 +- 0.10735707730054855 +max_val_scaled_root_mean_squared_error: +- 0.15188486874103546 +- 0.14977742731571198 +min_learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +min_loss: +- 0.021554268896579742 +- 0.02036287635564804 +min_scaled_mean_absolute_error: +- 0.012987556867301464 +- 0.012256953865289688 +min_scaled_root_mean_squared_error: +- 0.021794689819216728 +- 0.020948626101017 +min_val_loss: +- 0.1286155879497528 +- 0.13095012307167053 +min_val_scaled_mean_absolute_error: +- 0.07742822915315628 +- 0.07879547774791718 +min_val_scaled_root_mean_squared_error: +- 0.10747498273849487 +- 0.11117482930421829 +model_class: make_model +model_name: NMPN +model_version: '2023-11-22' +multi_target_indices: +- 5 +number_histories: 2 +scaled_mean_absolute_error: +- 0.01339768711477518 +- 0.012443263083696365 +scaled_root_mean_squared_error: +- 0.02263406291604042 +- 0.02139880508184433 +seed: 42 +time_list: +- '14:00:34.388450' +- '15:16:20.702028' +val_loss: +- 0.1397639513015747 +- 0.14023743569850922 +val_scaled_mean_absolute_error: +- 0.08415506780147552 +- 0.08439762145280838 +val_scaled_root_mean_squared_error: +- 0.11572442203760147 +- 0.11733932793140411 diff --git a/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_LUMO.yaml b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_LUMO.yaml new file mode 100644 index 00000000..010f2775 --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_QM9Dataset_score_LUMO.yaml @@ -0,0 +1,157 @@ +OS: posix_linux +backend: tensorflow +cuda_available: 'True' +data_unit: '[''eV'']' +date_time: '2024-03-14 09:14:24' +device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'', + device_type=''GPU'')]' +device_memory: '[]' +device_name: '[{}, {''compute_capability'': (7, 0), ''device_name'': ''Tesla V100-SXM2-32GB''}]' +epochs: +- 700 +- 700 +- 700 +- 700 +- 700 +execute_folds: +- 4 +kgcnn_version: 4.0.1 +learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +loss: +- 0.014469210989773273 +- 0.012984857894480228 +- 0.012219319120049477 +- 0.012345747090876102 +- 0.011789388954639435 +max_learning_rate: +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +- 9.999999747378752e-05 +max_loss: +- 0.32265859842300415 +- 0.3111254870891571 +- 0.3160345256328583 +- 0.31157881021499634 +- 0.3090427815914154 +max_scaled_mean_absolute_error: +- 0.4116283655166626 +- 0.39752092957496643 +- 0.40399420261383057 +- 0.3976333737373352 +- 0.39499327540397644 +max_scaled_root_mean_squared_error: +- 0.5742279291152954 +- 0.5611860752105713 +- 0.5724514126777649 +- 0.5612112879753113 +- 0.5584039092063904 +max_val_loss: +- 0.09120459854602814 +- 0.0904109850525856 +- 0.09009083360433578 +- 0.09452296793460846 +- 0.10098161548376083 +max_val_scaled_mean_absolute_error: +- 0.11618204414844513 +- 0.11543983221054077 +- 0.11499740928411484 +- 0.12048622220754623 +- 0.1289757937192917 +max_val_scaled_root_mean_squared_error: +- 0.16134732961654663 +- 0.1590588092803955 +- 0.1594996452331543 +- 0.16706445813179016 +- 0.1775275617837906 +min_learning_rate: +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +- 1.0138461220776662e-05 +min_loss: +- 0.014266223646700382 +- 0.012707773596048355 +- 0.012219319120049477 +- 0.012286314740777016 +- 0.011590874753892422 +min_scaled_mean_absolute_error: +- 0.018199913203716278 +- 0.0162364449352026 +- 0.015619536861777306 +- 0.01567956432700157 +- 0.014814416877925396 +min_scaled_root_mean_squared_error: +- 0.02976606972515583 +- 0.027980739250779152 +- 0.026513420045375824 +- 0.02748643048107624 +- 0.025099672377109528 +min_val_loss: +- 0.06485690176486969 +- 0.06460815668106079 +- 0.0638037770986557 +- 0.06254929304122925 +- 0.06680207699537277 +min_val_scaled_mean_absolute_error: +- 0.08260063081979752 +- 0.08248576521873474 +- 0.081427663564682 +- 0.07969919592142105 +- 0.08529137820005417 +min_val_scaled_root_mean_squared_error: +- 0.11747316271066666 +- 0.11537078022956848 +- 0.11493959277868271 +- 0.1117522343993187 +- 0.12272956967353821 +model_class: make_model +model_name: NMPN +model_version: '2023-11-22' +multi_target_indices: +- 6 +number_histories: 5 +scaled_mean_absolute_error: +- 0.018458805978298187 +- 0.01659047231078148 +- 0.015619536861777306 +- 0.015755310654640198 +- 0.01506815291941166 +scaled_root_mean_squared_error: +- 0.0305587537586689 +- 0.028680937364697456 +- 0.026601960882544518 +- 0.027729488909244537 +- 0.0255566593259573 +seed: 42 +time_list: +- '14:22:16.609634' +- '14:41:12.565808' +- '15:13:24.050872' +- '14:22:06.365005' +- '14:29:11.838977' +val_loss: +- 0.06952524185180664 +- 0.07019101083278656 +- 0.0680348351597786 +- 0.06758696585893631 +- 0.07146172225475311 +val_scaled_mean_absolute_error: +- 0.08859287202358246 +- 0.08961891382932663 +- 0.08682693541049957 +- 0.08610392361879349 +- 0.09124254435300827 +val_scaled_root_mean_squared_error: +- 0.1243230327963829 +- 0.12398459017276764 +- 0.12160081416368484 +- 0.1210021898150444 +- 0.13113437592983246 diff --git a/training/results/QM9Dataset/NMPN/NMPN_hyper_G.json b/training/results/QM9Dataset/NMPN/NMPN_hyper_G.json new file mode 100644 index 00000000..bfe762e9 --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_hyper_G.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.NMPN", "config": {"name": "NMPN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "input_edge_embedding": {"input_dim": 5, "output_dim": 64}, "set2set_args": {"channels": 32, "T": 3, "pooling_method": "sum", "init_qstar": "0"}, "pooling_args": {"pooling_method": "scatter_sum"}, "use_set2set": true, "depth": 3, "node_dim": 128, "verbose": 10, "geometric_edge": true, "make_distance": true, "expand_distance": true, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true, false], "units": [25, 25, 1], "activation": ["selu", "selu", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 700, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0001, "learning_rate_stop": 1e-05, "epo_min": 50, "epo": 700, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularLabelScaler", "config": {}}]}}, "multi_target_indices": [13]}, "data": {}, "info": {"postfix": "", "postfix_file": "_G", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 30}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/NMPN/NMPN_hyper_H.json b/training/results/QM9Dataset/NMPN/NMPN_hyper_H.json new file mode 100644 index 00000000..fa47b0a3 --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_hyper_H.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.NMPN", "config": {"name": "NMPN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "input_edge_embedding": {"input_dim": 5, "output_dim": 64}, "set2set_args": {"channels": 32, "T": 3, "pooling_method": "sum", "init_qstar": "0"}, "pooling_args": {"pooling_method": "scatter_sum"}, "use_set2set": true, "depth": 3, "node_dim": 128, "verbose": 10, "geometric_edge": true, "make_distance": true, "expand_distance": true, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true, false], "units": [25, 25, 1], "activation": ["selu", "selu", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 700, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0001, "learning_rate_stop": 1e-05, "epo_min": 50, "epo": 700, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularLabelScaler", "config": {}}]}}, "multi_target_indices": [12]}, "data": {}, "info": {"postfix": "", "postfix_file": "_H", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 30}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/NMPN/NMPN_hyper_HOMO.json b/training/results/QM9Dataset/NMPN/NMPN_hyper_HOMO.json new file mode 100644 index 00000000..a780f8d1 --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_hyper_HOMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.NMPN", "config": {"name": "NMPN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "input_edge_embedding": {"input_dim": 5, "output_dim": 64}, "set2set_args": {"channels": 32, "T": 3, "pooling_method": "sum", "init_qstar": "0"}, "pooling_args": {"pooling_method": "sum"}, "use_set2set": true, "depth": 3, "node_dim": 128, "verbose": 10, "geometric_edge": true, "make_distance": true, "expand_distance": true, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true, false], "units": [25, 25, 1], "activation": ["selu", "selu", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 700, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0001, "learning_rate_stop": 1e-05, "epo_min": 50, "epo": 700, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "StandardLabelScaler", "config": {"with_std": true, "with_mean": true, "copy": true}}]}}, "multi_target_indices": [5]}, "data": {}, "info": {"postfix": "", "postfix_file": "_HOMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 30}}]}} \ No newline at end of file diff --git a/training/results/QM9Dataset/NMPN/NMPN_hyper_LUMO.json b/training/results/QM9Dataset/NMPN/NMPN_hyper_LUMO.json new file mode 100644 index 00000000..c9b16b67 --- /dev/null +++ b/training/results/QM9Dataset/NMPN/NMPN_hyper_LUMO.json @@ -0,0 +1 @@ +{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.NMPN", "config": {"name": "NMPN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32", "ragged": true}, {"shape": [null, 2], "name": "edge_indices", "dtype": "int64", "ragged": true}], "input_tensor_type": "ragged", "input_embedding": null, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "input_edge_embedding": {"input_dim": 5, "output_dim": 64}, "set2set_args": {"channels": 32, "T": 3, "pooling_method": "sum", "init_qstar": "0"}, "pooling_args": {"pooling_method": "sum"}, "use_set2set": true, "depth": 3, "node_dim": 128, "verbose": 10, "geometric_edge": true, "make_distance": true, "expand_distance": true, "output_embedding": "graph", "output_mlp": {"use_bias": [true, true, false], "units": [25, 25, 1], "activation": ["selu", "selu", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 700, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0001, "learning_rate_stop": 1e-05, "epo_min": 50, "epo": 700, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0001}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "StandardLabelScaler", "config": {"with_std": true, "with_mean": true, "copy": true}}]}}, "multi_target_indices": [6]}, "data": {}, "info": {"postfix": "", "postfix_file": "_LUMO", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 4, "max_neighbours": 30}}]}} \ No newline at end of file