From be67e7035f67704f9e04f1b316029dae0f4940ab Mon Sep 17 00:00:00 2001
From: PatReis <pat.reiser@web.de>
Date: Mon, 1 Jan 2024 12:13:59 +0100
Subject: [PATCH] update training results.

---
 .../PAiNN/PAiNN_QM9Dataset_score_H.yaml       | 139 ++++++++++++++++++
 .../PAiNN/PAiNN_QM9Dataset_score_LUMO.yaml    | 139 ++++++++++++++++++
 .../QM9Dataset/PAiNN/PAiNN_hyper_H.json       |   1 +
 .../QM9Dataset/PAiNN/PAiNN_hyper_LUMO.json    |   1 +
 4 files changed, 280 insertions(+)
 create mode 100644 training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_H.yaml
 create mode 100644 training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_LUMO.yaml
 create mode 100644 training/results/QM9Dataset/PAiNN/PAiNN_hyper_H.json
 create mode 100644 training/results/QM9Dataset/PAiNN/PAiNN_hyper_LUMO.json

diff --git a/training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_H.yaml b/training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_H.yaml
new file mode 100644
index 00000000..e0843dac
--- /dev/null
+++ b/training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_H.yaml
@@ -0,0 +1,139 @@
+OS: posix_linux
+backend: tensorflow
+cuda_available: 'True'
+data_unit: '[''eV'']'
+date_time: '2023-12-30 14:40:05'
+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:
+- 872
+- 872
+- 872
+- 872
+- 872
+execute_folds:
+- 3
+kgcnn_version: 4.0.0
+loss:
+- 0.0019955458119511604
+- 0.002024617977440357
+- 0.0019782360177487135
+- 0.0019529580604285002
+- 0.0019817312713712454
+max_loss:
+- 0.18526189029216766
+- 0.18828505277633667
+- 0.18873144686222076
+- 0.1903804987668991
+- 0.18624798953533173
+max_scaled_mean_absolute_error:
+- 0.20193131268024445
+- 0.2055528163909912
+- 0.20597510039806366
+- 0.20785720646381378
+- 0.20349407196044922
+max_scaled_root_mean_squared_error:
+- 0.3394524157047272
+- 0.3438010513782501
+- 0.3476903438568115
+- 0.3472926616668701
+- 0.34120410680770874
+max_val_loss:
+- 0.03188300132751465
+- 0.029626354575157166
+- 0.03657916933298111
+- 0.047557979822158813
+- 0.04267334192991257
+max_val_scaled_mean_absolute_error:
+- 0.03464236482977867
+- 0.032236188650131226
+- 0.03979366645216942
+- 0.05186860263347626
+- 0.046537794172763824
+max_val_scaled_root_mean_squared_error:
+- 0.05618090182542801
+- 0.05501886457204819
+- 0.06330189853906631
+- 0.07451926171779633
+- 0.0724678486585617
+min_loss:
+- 0.0019673604983836412
+- 0.0019799822475761175
+- 0.0019443101482465863
+- 0.0019377947319298983
+- 0.0019342212472110987
+min_scaled_mean_absolute_error:
+- 0.0021442738361656666
+- 0.0021614376455545425
+- 0.0021219186019152403
+- 0.0021156545262783766
+- 0.002113256836310029
+min_scaled_root_mean_squared_error:
+- 0.0028910746332257986
+- 0.0029227002523839474
+- 0.0028797462582588196
+- 0.002844379749149084
+- 0.002818490844219923
+min_val_loss:
+- 0.009148568846285343
+- 0.008913757279515266
+- 0.009337415918707848
+- 0.008778532966971397
+- 0.00909003708511591
+min_val_scaled_mean_absolute_error:
+- 0.009881334379315376
+- 0.009660759940743446
+- 0.010099595412611961
+- 0.009524621069431305
+- 0.009874518029391766
+min_val_scaled_root_mean_squared_error:
+- 0.029311876744031906
+- 0.02768424153327942
+- 0.030961958691477776
+- 0.02643650770187378
+- 0.028908340260386467
+model_class: make_model
+model_name: PAiNN
+model_version: '2023-10-04'
+multi_target_indices:
+- 12
+number_histories: 5
+scaled_mean_absolute_error:
+- 0.0021750417072325945
+- 0.0022102019283920527
+- 0.0021589179523289204
+- 0.0021322108805179596
+- 0.0021651547867804766
+scaled_root_mean_squared_error:
+- 0.002943216124549508
+- 0.0029651967342942953
+- 0.0029119839891791344
+- 0.0028563514351844788
+- 0.0028814643155783415
+seed: 42
+time_list:
+- '15:51:51.633536'
+- '15:45:18.577087'
+- '15:40:21.663428'
+- '15:49:08.423918'
+- '15:31:42.287163'
+val_loss:
+- 0.009798742830753326
+- 0.009327730163931847
+- 0.009464696981012821
+- 0.008801082149147987
+- 0.009116045199334621
+val_scaled_mean_absolute_error:
+- 0.01059046946465969
+- 0.010111944749951363
+- 0.010241112671792507
+- 0.009549328126013279
+- 0.009903214871883392
+val_scaled_root_mean_squared_error:
+- 0.029764937236905098
+- 0.028448030352592468
+- 0.034340400248765945
+- 0.026546387001872063
+- 0.029275482520461082
diff --git a/training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_LUMO.yaml b/training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_LUMO.yaml
new file mode 100644
index 00000000..ac8822ba
--- /dev/null
+++ b/training/results/QM9Dataset/PAiNN/PAiNN_QM9Dataset_score_LUMO.yaml
@@ -0,0 +1,139 @@
+OS: posix_linux
+backend: tensorflow
+cuda_available: 'True'
+data_unit: '[''eV'']'
+date_time: '2024-01-01 09:57:09'
+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:
+- 872
+- 872
+- 872
+- 872
+- 872
+execute_folds:
+- 4
+kgcnn_version: 4.0.0
+loss:
+- 0.0020861534867435694
+- 0.0019487738609313965
+- 0.0019303049193695188
+- 0.0019613842014223337
+- 0.001908505568280816
+max_loss:
+- 0.20696552097797394
+- 0.20219039916992188
+- 0.20419193804264069
+- 0.20342102646827698
+- 0.20268265902996063
+max_scaled_mean_absolute_error:
+- 0.2640361785888672
+- 0.25833627581596375
+- 0.2610222399234772
+- 0.25960054993629456
+- 0.2590479850769043
+max_scaled_root_mean_squared_error:
+- 0.39627182483673096
+- 0.3863120377063751
+- 0.39248400926589966
+- 0.3890867829322815
+- 0.3875385522842407
+max_val_loss:
+- 0.047063447535037994
+- 0.05677426606416702
+- 0.04467691853642464
+- 0.050913263112306595
+- 0.054120130836963654
+max_val_scaled_mean_absolute_error:
+- 0.05994651839137077
+- 0.07244302332401276
+- 0.05697048082947731
+- 0.06481605023145676
+- 0.06905586272478104
+max_val_scaled_root_mean_squared_error:
+- 0.08429275453090668
+- 0.09583966434001923
+- 0.08529383689165115
+- 0.09227821975946426
+- 0.10410711169242859
+min_loss:
+- 0.0020580007694661617
+- 0.0019487738609313965
+- 0.001929765217937529
+- 0.001947144977748394
+- 0.001908505568280816
+min_scaled_mean_absolute_error:
+- 0.002625466324388981
+- 0.0024898583069443703
+- 0.0024668066762387753
+- 0.002484900178387761
+- 0.002439181786030531
+min_scaled_root_mean_squared_error:
+- 0.003604347351938486
+- 0.003451655153185129
+- 0.003394855884835124
+- 0.003470573341473937
+- 0.003339767688885331
+min_val_loss:
+- 0.020965995267033577
+- 0.020821647718548775
+- 0.02085803635418415
+- 0.020851779729127884
+- 0.020947162061929703
+min_val_scaled_mean_absolute_error:
+- 0.02664823830127716
+- 0.026541169732809067
+- 0.026546595618128777
+- 0.026502860710024834
+- 0.026716047897934914
+min_val_scaled_root_mean_squared_error:
+- 0.0506107471883297
+- 0.047274112701416016
+- 0.04923555999994278
+- 0.05022411420941353
+- 0.055102452635765076
+model_class: make_model
+model_name: PAiNN
+model_version: '2023-10-04'
+multi_target_indices:
+- 6
+number_histories: 5
+scaled_mean_absolute_error:
+- 0.0026613834779709578
+- 0.0024898583069443703
+- 0.0024674590677022934
+- 0.002503048861399293
+- 0.002439181786030531
+scaled_root_mean_squared_error:
+- 0.0036517123226076365
+- 0.003451655153185129
+- 0.003394855884835124
+- 0.003490382805466652
+- 0.003339767688885331
+seed: 42
+time_list:
+- '15:24:04.894363'
+- '15:57:32.449881'
+- '15:35:41.391352'
+- '14:49:24.468281'
+- '15:15:03.458031'
+val_loss:
+- 0.02117057330906391
+- 0.020858103409409523
+- 0.02085803635418415
+- 0.02103325165808201
+- 0.021233005449175835
+val_scaled_mean_absolute_error:
+- 0.026909509673714638
+- 0.02658955752849579
+- 0.026546595618128777
+- 0.02673463709652424
+- 0.027080604806542397
+val_scaled_root_mean_squared_error:
+- 0.05089842155575752
+- 0.04728246480226517
+- 0.04927689954638481
+- 0.0504501610994339
+- 0.05542372912168503
diff --git a/training/results/QM9Dataset/PAiNN/PAiNN_hyper_H.json b/training/results/QM9Dataset/PAiNN/PAiNN_hyper_H.json
new file mode 100644
index 00000000..692b2899
--- /dev/null
+++ b/training/results/QM9Dataset/PAiNN/PAiNN_hyper_H.json
@@ -0,0 +1 @@
+{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.PAiNN", "config": {"name": "PAiNN", "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}], "input_tensor_type": "ragged", "input_embedding": null, "equiv_initialize_kwargs": {"dim": 3, "method": "eps", "units": 128}, "input_node_embedding": {"input_dim": 95, "output_dim": 128}, "bessel_basis": {"num_radial": 20, "cutoff": 5.0, "envelope_exponent": 5}, "pooling_args": {"pooling_method": "sum"}, "conv_args": {"units": 128, "cutoff": null}, "update_args": {"units": 128, "add_eps": true}, "depth": 3, "verbose": 10, "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": 872, "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}}, "amsgrad": true, "use_ema": true}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "QMGraphLabelScaler", "config": {"atomic_number": "node_number", "scaler": [{"class_name": "ExtensiveMolecularScaler", "config": {}}]}}, "multi_target_indices": [12]}, "dataset": {"class_name": "QM9Dataset", "module_name": "kgcnn.data.datasets.QM9Dataset", "config": {}, "methods": [{"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 10000}}]}, "data": {}, "info": {"postfix": "", "postfix_file": "_H", "kgcnn_version": "4.0.0"}}
\ No newline at end of file
diff --git a/training/results/QM9Dataset/PAiNN/PAiNN_hyper_LUMO.json b/training/results/QM9Dataset/PAiNN/PAiNN_hyper_LUMO.json
new file mode 100644
index 00000000..680f0fdc
--- /dev/null
+++ b/training/results/QM9Dataset/PAiNN/PAiNN_hyper_LUMO.json
@@ -0,0 +1 @@
+{"model": {"class_name": "make_model", "module_name": "kgcnn.literature.PAiNN", "config": {"name": "PAiNN", "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}], "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}, "pooling_args": {"pooling_method": "sum"}, "conv_args": {"units": 128, "cutoff": null}, "update_args": {"units": 128}, "depth": 3, "verbose": 10, "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": 872, "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}}, "amsgrad": true, "use_ema": 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": 10000}}]}}
\ No newline at end of file