Updated training results

aimat-lab · Mar 25, 2024 · 3569223 · 3569223
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diff --git a/...MatProjectEFormDataset/Megnet_make_crystal_model/Megnet_MatProjectEFormDataset_score.yaml b/...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:
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+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:
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+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
@@ -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}}]}}
diff --git a/...s/MatProjectEFormDataset/PAiNN_make_crystal_model/PAiNN_MatProjectEFormDataset_score.yaml b/...s/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
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+- 9.999999747378752e-05
+- 9.999999747378752e-05
+- 9.999999747378752e-05
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+multi_target_indices: null
+number_histories: 5
+scaled_mean_absolute_error:
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+seed: 42
+time_list:
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+- 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
@@ -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}}]}}
diff --git a/training/results/QM9Dataset/DimeNetPP/DimeNetPP_QM9Dataset_score_G.yaml 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
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+multi_target_indices:
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+number_histories: 5
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+- 1 day, 10:18:30.398631
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