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update results of training. Fix hyper settings.
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@PatReis
PatReis committed Dec 31, 2023
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2 changes: 1 addition & 1 deletion training/hyper/hyper_mp_perovskites.py
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"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": "segment_mean"},
"pooling_args": {"pooling_method": "mean"},
"use_set2set": True,
"depth": 3,
"node_dim": 128,
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156 changes: 156 additions & 0 deletions ...PerovskitesDataset/CGCNN_make_crystal_model/CGCNN_MatProjectPerovskitesDataset_score.yaml
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OS: posix_linux
backend: tensorflow
cuda_available: 'True'
data_unit: eV/unit_cell
date_time: '2023-12-29 17:10:17'
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
- 1000
- 1000
- 1000
- 1000
execute_folds: null
kgcnn_version: 4.0.0
learning_rate:
- 1.1979999726463575e-05
- 1.1979999726463575e-05
- 1.1979999726463575e-05
- 1.1979999726463575e-05
- 1.1979999726463575e-05
loss:
- 0.0034721200354397297
- 0.0031347335316240788
- 0.003559909062460065
- 0.0026034489274024963
- 0.0029621929861605167
max_learning_rate:
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
max_loss:
- 0.6292704343795776
- 0.6259264945983887
- 0.6165923476219177
- 0.621681272983551
- 0.6198083758354187
max_scaled_mean_absolute_error:
- 0.46660152077674866
- 0.46269047260284424
- 0.4589654505252838
- 0.4626944065093994
- 0.4626486599445343
max_scaled_root_mean_squared_error:
- 0.6282691955566406
- 0.6173467040061951
- 0.6106275320053101
- 0.6159034371376038
- 0.6166854500770569
max_val_loss:
- 0.2256670445203781
- 0.18095774948596954
- 0.17906711995601654
- 0.17330452799797058
- 0.18813487887382507
max_val_scaled_mean_absolute_error:
- 0.16691288352012634
- 0.13376717269420624
- 0.13307134807109833
- 0.1291094720363617
- 0.14027859270572662
max_val_scaled_root_mean_squared_error:
- 0.21514976024627686
- 0.17830561101436615
- 0.17366617918014526
- 0.16985341906547546
- 0.18020832538604736
min_learning_rate:
- 1.1979999726463575e-05
- 1.1979999726463575e-05
- 1.1979999726463575e-05
- 1.1979999726463575e-05
- 1.1979999726463575e-05
min_loss:
- 0.003272873582318425
- 0.0031347335316240788
- 0.003559909062460065
- 0.001684526796452701
- 0.002604327630251646
min_scaled_mean_absolute_error:
- 0.0024318417999893427
- 0.002321885898709297
- 0.00265521090477705
- 0.0012572073610499501
- 0.0019469260005280375
min_scaled_root_mean_squared_error:
- 0.0032979012466967106
- 0.004462615121155977
- 0.0035071931779384613
- 0.001812510658055544
- 0.002829661127179861
min_val_loss:
- 0.0576678104698658
- 0.057317446917295456
- 0.05787668749690056
- 0.05293188616633415
- 0.058691468089818954
min_val_scaled_mean_absolute_error:
- 0.0427803136408329
- 0.04239288717508316
- 0.04310505464673042
- 0.03933073207736015
- 0.04357030615210533
min_val_scaled_root_mean_squared_error:
- 0.06776996701955795
- 0.06877703219652176
- 0.07191338390111923
- 0.0630967915058136
- 0.07034579664468765
model_class: make_crystal_model
model_name: CGCNN
model_version: '2023-11-28'
multi_target_indices: null
number_histories: 5
scaled_mean_absolute_error:
- 0.002574356272816658
- 0.002321885898709297
- 0.00265521090477705
- 0.0019408881198614836
- 0.0022164378315210342
scaled_root_mean_squared_error:
- 0.0033517011906951666
- 0.004462615121155977
- 0.0035071931779384613
- 0.0026335762813687325
- 0.002941363723948598
seed: 42
time_list:
- '0:45:32.200729'
- '0:45:37.339951'
- '0:45:36.867218'
- '0:45:07.615956'
- '0:45:17.050593'
val_loss:
- 0.05813664570450783
- 0.05748477950692177
- 0.05787668749690056
- 0.05435239151120186
- 0.058691468089818954
val_scaled_mean_absolute_error:
- 0.04315347597002983
- 0.042528096586465836
- 0.04310505464673042
- 0.04035795480012894
- 0.04357030615210533
val_scaled_root_mean_squared_error:
- 0.07042886316776276
- 0.07220316678285599
- 0.07241115719079971
- 0.06471456587314606
- 0.07611246407032013
1 change: 1 addition & 0 deletions training/results/MatProjectPerovskitesDataset/CGCNN_make_crystal_model/CGCNN_hyper.json
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{"model": {"class_name": "make_crystal_model", "module_name": "kgcnn.literature.CGCNN", "config": {"name": "CGCNN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_frac_coordinates", "dtype": "float64", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "range_image", "dtype": "float32", "ragged": true}, {"shape": [3, 3], "name": "graph_lattice", "dtype": "float64", "ragged": false}], "input_tensor_type": "ragged", "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "representation": "unit", "expand_distance": true, "make_distances": true, "gauss_args": {"bins": 60, "distance": 6, "offset": 0.0, "sigma": 0.4}, "conv_layer_args": {"units": 128, "activation_s": "kgcnn>shifted_softplus", "activation_out": "kgcnn>shifted_softplus", "batch_normalization": true}, "node_pooling_args": {"pooling_method": "mean"}, "depth": 4, "output_mlp": {"use_bias": [true, true, false], "units": [128, 64, 1], "activation": ["kgcnn>shifted_softplus", "kgcnn>shifted_softplus", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 1000, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.001, "learning_rate_stop": 1e-05, "epo_min": 500, "epo": 1000, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.001}}, "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/unit_cell"}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "MatProjectPerovskitesDataset", "module_name": "kgcnn.data.datasets.MatProjectPerovskitesDataset", "config": {}, "methods": [{"map_list": {"method": "set_range_periodic", "max_distance": 6.0}}]}}

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