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fix hyperparameter for mp_gap.
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@PatReis
PatReis committed Jan 11, 2024
1 parent 469bed1 commit 48004cf
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8 changes: 4 additions & 4 deletions training/hyper/hyper_mp_gap.py
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Expand Up @@ -116,7 +116,7 @@
},
"scaler": {
"class_name": "StandardLabelScaler",
"module_name": "kgcnn.data.transform.scaler.scaler",
"module_name": "kgcnn.data.transform.scaler.standard",
"config": {"with_std": True, "with_mean": True, "copy": True}
},
"multi_target_indices": None
Expand Down Expand Up @@ -189,7 +189,7 @@
},
"scaler": {
"class_name": "StandardLabelScaler",
"module_name": "kgcnn.data.transform.scaler.scaler",
"module_name": "kgcnn.data.transform.scaler.standard",
"config": {"with_std": True, "with_mean": True, "copy": True}
},
"multi_target_indices": None
Expand Down Expand Up @@ -267,7 +267,7 @@
},
"scaler": {
"class_name": "StandardLabelScaler",
"module_name": "kgcnn.data.transform.scaler.scaler",
"module_name": "kgcnn.data.transform.scaler.standard",
"config": {"with_std": True, "with_mean": True, "copy": True}
},
"multi_target_indices": None
Expand Down Expand Up @@ -341,7 +341,7 @@
},
"scaler": {
"class_name": "StandardLabelScaler",
"module_name": "kgcnn.data.transform.scaler.scaler",
"module_name": "kgcnn.data.transform.scaler.standard",
"config": {"with_std": True, "with_mean": True, "copy": True}
},
"multi_target_indices": None
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157 changes: 157 additions & 0 deletions ...tProjectIsMetalDataset/CGCNN_make_crystal_model/CGCNN_MatProjectIsMetalDataset_score.yaml
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OS: posix_linux
auc:
- 0.9948922991752625
- 0.9952804446220398
- 0.9954200983047485
- 0.9949931502342224
- 0.995144784450531
backend: tensorflow
binary_accuracy:
- 0.9641535878181458
- 0.9658499360084534
- 0.9677700400352478
- 0.9645780920982361
- 0.966121256351471
cuda_available: 'True'
data_unit: ''
date_time: '2024-01-09 13:54:56'
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:
- 100
- 100
- 100
- 100
- 100
execute_folds:
- 4
kgcnn_version: 4.0.0
learning_rate:
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
loss:
- 0.08773165196180344
- 0.08345693349838257
- 0.08153712004423141
- 0.08709339052438736
- 0.0846143513917923
max_auc:
- 0.9951267838478088
- 0.9952804446220398
- 0.9954200983047485
- 0.9949931502342224
- 0.9952813982963562
max_binary_accuracy:
- 0.9653433561325073
- 0.9658499360084534
- 0.9677700400352478
- 0.9647430181503296
- 0.9664393067359924
max_learning_rate:
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
max_loss:
- 0.3875212073326111
- 0.38699889183044434
- 0.38575437664985657
- 0.3867986798286438
- 0.388640433549881
max_val_auc:
- 0.9513524174690247
- 0.9519103765487671
- 0.9466461539268494
- 0.9484876394271851
- 0.9518027901649475
max_val_binary_accuracy:
- 0.8958205580711365
- 0.8999199271202087
- 0.8910616040229797
- 0.895061731338501
- 0.8952973484992981
max_val_loss:
- 0.49062415957450867
- 0.46342551708221436
- 0.4951637089252472
- 0.46463239192962646
- 0.467955082654953
min_auc:
- 0.8970277905464172
- 0.8969246745109558
- 0.8974226117134094
- 0.8976449370384216
- 0.8958519101142883
min_binary_accuracy:
- 0.8304982781410217
- 0.8312286734580994
- 0.8328425288200378
- 0.8320081233978271
- 0.830417811870575
min_learning_rate:
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
- 0.0010000000474974513
min_loss:
- 0.08625432103872299
- 0.08345693349838257
- 0.08153712004423141
- 0.08678771555423737
- 0.0836603119969368
min_val_auc:
- 0.9385629892349243
- 0.9415743350982666
- 0.9372962713241577
- 0.9409415125846863
- 0.9418054819107056
min_val_binary_accuracy:
- 0.8791877031326294
- 0.8813551068305969
- 0.8718371391296387
- 0.8743285536766052
- 0.8812553286552429
min_val_loss:
- 0.2944905161857605
- 0.274251788854599
- 0.30542659759521484
- 0.2915760576725006
- 0.28835123777389526
model_class: make_crystal_model
model_name: CGCNN
model_version: '2023-11-28'
multi_target_indices: null
number_histories: 5
seed: 42
time_list:
- '1:28:30.933029'
- '1:27:43.832826'
- '1:28:37.538104'
- '1:28:30.700542'
- '1:27:44.145208'
val_auc:
- 0.9385629892349243
- 0.9417924880981445
- 0.9372962713241577
- 0.9437304139137268
- 0.9418054819107056
val_binary_accuracy:
- 0.8906375169754028
- 0.8939829468727112
- 0.8862555027008057
- 0.8906323909759521
- 0.893318235874176
val_loss:
- 0.49062415957450867
- 0.46342551708221436
- 0.4951637089252472
- 0.41519537568092346
- 0.467955082654953
1 change: 1 addition & 0 deletions training/results/MatProjectIsMetalDataset/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", "sigmoid"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 100, "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": 100, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.001}}, "loss": "binary_crossentropy", "metrics": [{"class_name": "AUC", "config": {"name": "auc"}}, "binary_accuracy"]}, "multi_target_indices": null}, "data": {"data_unit": ""}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "MatProjectIsMetalDataset", "module_name": "kgcnn.data.datasets.MatProjectIsMetalDataset", "config": {}, "methods": [{"map_list": {"method": "set_range_periodic", "max_distance": 6.0}}]}}

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