add pretrained models

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/100e/train_res

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+Epoch: 000, Train Loss: 0.048970, Test Loss: 0.048667
+Epoch: 001, Train Loss: 0.030800, Test Loss: 0.030511
+Epoch: 002, Train Loss: 0.025334, Test Loss: 0.025270
+Epoch: 003, Train Loss: 0.023651, Test Loss: 0.023538
+Epoch: 004, Train Loss: 0.019162, Test Loss: 0.019080
+Epoch: 005, Train Loss: 0.019853, Test Loss: 0.019819
+Epoch: 006, Train Loss: 0.014548, Test Loss: 0.014642
+Epoch: 007, Train Loss: 0.015340, Test Loss: 0.015407
+Epoch: 008, Train Loss: 0.013076, Test Loss: 0.013189
+Epoch: 009, Train Loss: 0.010759, Test Loss: 0.010827
+Epoch: 010, Train Loss: 0.009676, Test Loss: 0.009796
+Epoch: 011, Train Loss: 0.009729, Test Loss: 0.009877
+Epoch: 012, Train Loss: 0.009505, Test Loss: 0.009588
+Epoch: 013, Train Loss: 0.007938, Test Loss: 0.008046
+Epoch: 014, Train Loss: 0.007638, Test Loss: 0.007730
+Epoch: 015, Train Loss: 0.007149, Test Loss: 0.007246
+Epoch: 016, Train Loss: 0.007712, Test Loss: 0.007853
+Epoch: 017, Train Loss: 0.007507, Test Loss: 0.007556
+Epoch: 018, Train Loss: 0.006890, Test Loss: 0.006997
+Epoch: 019, Train Loss: 0.006902, Test Loss: 0.007011
+Epoch: 020, Train Loss: 0.006120, Test Loss: 0.006238
+Epoch: 021, Train Loss: 0.006021, Test Loss: 0.006144
+Epoch: 022, Train Loss: 0.005587, Test Loss: 0.005711
+Epoch: 023, Train Loss: 0.005837, Test Loss: 0.005951
+Epoch: 024, Train Loss: 0.005759, Test Loss: 0.005873
+Epoch: 025, Train Loss: 0.005572, Test Loss: 0.005677
+Epoch: 026, Train Loss: 0.005298, Test Loss: 0.005421
+Epoch: 027, Train Loss: 0.005178, Test Loss: 0.005298
+Epoch: 028, Train Loss: 0.005297, Test Loss: 0.005416
+Epoch: 029, Train Loss: 0.005530, Test Loss: 0.005629
+Epoch: 030, Train Loss: 0.005050, Test Loss: 0.005169
+Epoch: 031, Train Loss: 0.005010, Test Loss: 0.005124
+Epoch: 032, Train Loss: 0.005088, Test Loss: 0.005215
+Epoch: 033, Train Loss: 0.004981, Test Loss: 0.005100
+Epoch: 034, Train Loss: 0.004784, Test Loss: 0.004912
+Epoch: 035, Train Loss: 0.004354, Test Loss: 0.004489
+Epoch: 036, Train Loss: 0.004310, Test Loss: 0.004452
+Epoch: 037, Train Loss: 0.004201, Test Loss: 0.004338
+Epoch: 038, Train Loss: 0.004280, Test Loss: 0.004434
+Epoch: 039, Train Loss: 0.004249, Test Loss: 0.004401
+Epoch: 040, Train Loss: 0.004414, Test Loss: 0.004560
+Epoch: 041, Train Loss: 0.004089, Test Loss: 0.004244
+Epoch: 042, Train Loss: 0.004175, Test Loss: 0.004347
+Epoch: 043, Train Loss: 0.004278, Test Loss: 0.004436
+Epoch: 044, Train Loss: 0.004163, Test Loss: 0.004314
+Epoch: 045, Train Loss: 0.004047, Test Loss: 0.004236
+Epoch: 046, Train Loss: 0.004052, Test Loss: 0.004219
+Epoch: 047, Train Loss: 0.003960, Test Loss: 0.004133
+Epoch: 048, Train Loss: 0.003730, Test Loss: 0.003902
+Epoch: 049, Train Loss: 0.003869, Test Loss: 0.004039
+Epoch: 050, Train Loss: 0.003696, Test Loss: 0.003866
+Epoch: 051, Train Loss: 0.003892, Test Loss: 0.004041
+Epoch: 052, Train Loss: 0.004020, Test Loss: 0.004185
+Epoch: 053, Train Loss: 0.003810, Test Loss: 0.003983
+Epoch: 054, Train Loss: 0.003761, Test Loss: 0.003915
+Epoch: 055, Train Loss: 0.003905, Test Loss: 0.004070
+Epoch: 056, Train Loss: 0.003691, Test Loss: 0.003864
+Epoch: 057, Train Loss: 0.003878, Test Loss: 0.004045
+Epoch: 058, Train Loss: 0.003448, Test Loss: 0.003622
+Epoch: 059, Train Loss: 0.003818, Test Loss: 0.003982
+Epoch: 060, Train Loss: 0.003715, Test Loss: 0.003885
+Epoch: 061, Train Loss: 0.003829, Test Loss: 0.003994
+Epoch: 062, Train Loss: 0.004003, Test Loss: 0.004183
+Epoch: 063, Train Loss: 0.003527, Test Loss: 0.003699
+Epoch: 064, Train Loss: 0.003164, Test Loss: 0.003315
+Epoch: 065, Train Loss: 0.003364, Test Loss: 0.003543
+Epoch: 066, Train Loss: 0.003863, Test Loss: 0.004034
+Epoch: 067, Train Loss: 0.003042, Test Loss: 0.003210
+Epoch: 068, Train Loss: 0.003170, Test Loss: 0.003324
+Epoch: 069, Train Loss: 0.003216, Test Loss: 0.003375
+Epoch: 070, Train Loss: 0.003173, Test Loss: 0.003336
+Epoch: 071, Train Loss: 0.002911, Test Loss: 0.003069
+Epoch: 072, Train Loss: 0.003125, Test Loss: 0.003285
+Epoch: 073, Train Loss: 0.003070, Test Loss: 0.003210
+Epoch: 074, Train Loss: 0.002740, Test Loss: 0.002884
+Epoch: 075, Train Loss: 0.003088, Test Loss: 0.003235
+Epoch: 076, Train Loss: 0.003172, Test Loss: 0.003334
+Epoch: 077, Train Loss: 0.002919, Test Loss: 0.003071
+Epoch: 078, Train Loss: 0.002999, Test Loss: 0.003141
+Epoch: 079, Train Loss: 0.003031, Test Loss: 0.003189
+Epoch: 080, Train Loss: 0.002906, Test Loss: 0.003084
+Epoch: 081, Train Loss: 0.002974, Test Loss: 0.003123
+Epoch: 082, Train Loss: 0.002798, Test Loss: 0.002937
+Epoch: 083, Train Loss: 0.003092, Test Loss: 0.003233
+Epoch: 084, Train Loss: 0.003443, Test Loss: 0.003602
+Epoch: 085, Train Loss: 0.002840, Test Loss: 0.002990
+Epoch: 086, Train Loss: 0.002805, Test Loss: 0.002959
+Epoch: 087, Train Loss: 0.002878, Test Loss: 0.003014
+Epoch: 088, Train Loss: 0.002929, Test Loss: 0.003086
+Epoch: 089, Train Loss: 0.002926, Test Loss: 0.003084
+Epoch: 090, Train Loss: 0.002785, Test Loss: 0.002938
+Epoch: 091, Train Loss: 0.002726, Test Loss: 0.002869
+Epoch: 092, Train Loss: 0.003428, Test Loss: 0.003573
+Epoch: 093, Train Loss: 0.003024, Test Loss: 0.003174
+Epoch: 094, Train Loss: 0.002989, Test Loss: 0.003127
+Epoch: 095, Train Loss: 0.002801, Test Loss: 0.002942
+Epoch: 096, Train Loss: 0.003118, Test Loss: 0.003258
+Epoch: 097, Train Loss: 0.003105, Test Loss: 0.003258
+Epoch: 098, Train Loss: 0.002740, Test Loss: 0.002885
+Epoch: 099, Train Loss: 0.002515, Test Loss: 0.002646

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/20e/train_res

@@ -0,0 +1,20 @@
+Epoch: 000, Train Loss: 0.050863, Test Loss: 0.052546
+Epoch: 001, Train Loss: 0.029373, Test Loss: 0.030833
+Epoch: 002, Train Loss: 0.020787, Test Loss: 0.021997
+Epoch: 003, Train Loss: 0.016364, Test Loss: 0.017456
+Epoch: 004, Train Loss: 0.014149, Test Loss: 0.015224
+Epoch: 005, Train Loss: 0.012573, Test Loss: 0.013564
+Epoch: 006, Train Loss: 0.010724, Test Loss: 0.011628
+Epoch: 007, Train Loss: 0.009498, Test Loss: 0.010333
+Epoch: 008, Train Loss: 0.008883, Test Loss: 0.009697
+Epoch: 009, Train Loss: 0.008178, Test Loss: 0.008891
+Epoch: 010, Train Loss: 0.007793, Test Loss: 0.008484
+Epoch: 011, Train Loss: 0.007520, Test Loss: 0.008133
+Epoch: 012, Train Loss: 0.007100, Test Loss: 0.007761
+Epoch: 013, Train Loss: 0.006461, Test Loss: 0.007043
+Epoch: 014, Train Loss: 0.006458, Test Loss: 0.007039
+Epoch: 015, Train Loss: 0.006238, Test Loss: 0.006798
+Epoch: 016, Train Loss: 0.006034, Test Loss: 0.006582
+Epoch: 017, Train Loss: 0.005676, Test Loss: 0.006194
+Epoch: 018, Train Loss: 0.005672, Test Loss: 0.006189
+Epoch: 019, Train Loss: 0.005481, Test Loss: 0.005976

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/64ch/50e/train_res

@@ -0,0 +1,50 @@
+Epoch: 000, Train Loss: 0.057171, Test Loss: 0.058010
+Epoch: 001, Train Loss: 0.030787, Test Loss: 0.031484
+Epoch: 002, Train Loss: 0.023136, Test Loss: 0.023673
+Epoch: 003, Train Loss: 0.019109, Test Loss: 0.019526
+Epoch: 004, Train Loss: 0.017071, Test Loss: 0.017409
+Epoch: 005, Train Loss: 0.014807, Test Loss: 0.015129
+Epoch: 006, Train Loss: 0.014352, Test Loss: 0.014680
+Epoch: 007, Train Loss: 0.012505, Test Loss: 0.012756
+Epoch: 008, Train Loss: 0.011339, Test Loss: 0.011526
+Epoch: 009, Train Loss: 0.011503, Test Loss: 0.011724
+Epoch: 010, Train Loss: 0.009757, Test Loss: 0.009975
+Epoch: 011, Train Loss: 0.009136, Test Loss: 0.009331
+Epoch: 012, Train Loss: 0.009876, Test Loss: 0.010134
+Epoch: 013, Train Loss: 0.009290, Test Loss: 0.009502
+Epoch: 014, Train Loss: 0.007732, Test Loss: 0.007969
+Epoch: 015, Train Loss: 0.008310, Test Loss: 0.008550
+Epoch: 016, Train Loss: 0.007947, Test Loss: 0.008159
+Epoch: 017, Train Loss: 0.007175, Test Loss: 0.007429
+Epoch: 018, Train Loss: 0.006615, Test Loss: 0.006852
+Epoch: 019, Train Loss: 0.006258, Test Loss: 0.006468
+Epoch: 020, Train Loss: 0.006021, Test Loss: 0.006213
+Epoch: 021, Train Loss: 0.005957, Test Loss: 0.006177
+Epoch: 022, Train Loss: 0.005714, Test Loss: 0.005900
+Epoch: 023, Train Loss: 0.005592, Test Loss: 0.005798
+Epoch: 024, Train Loss: 0.005693, Test Loss: 0.005882
+Epoch: 025, Train Loss: 0.005862, Test Loss: 0.006065
+Epoch: 026, Train Loss: 0.005933, Test Loss: 0.006124
+Epoch: 027, Train Loss: 0.004832, Test Loss: 0.004976
+Epoch: 028, Train Loss: 0.005156, Test Loss: 0.005320
+Epoch: 029, Train Loss: 0.004828, Test Loss: 0.004969
+Epoch: 030, Train Loss: 0.004528, Test Loss: 0.004665
+Epoch: 031, Train Loss: 0.004441, Test Loss: 0.004558
+Epoch: 032, Train Loss: 0.004391, Test Loss: 0.004523
+Epoch: 033, Train Loss: 0.004387, Test Loss: 0.004529
+Epoch: 034, Train Loss: 0.004330, Test Loss: 0.004454
+Epoch: 035, Train Loss: 0.004358, Test Loss: 0.004487
+Epoch: 036, Train Loss: 0.004368, Test Loss: 0.004527
+Epoch: 037, Train Loss: 0.004413, Test Loss: 0.004563
+Epoch: 038, Train Loss: 0.004288, Test Loss: 0.004420
+Epoch: 039, Train Loss: 0.004150, Test Loss: 0.004276
+Epoch: 040, Train Loss: 0.004238, Test Loss: 0.004380
+Epoch: 041, Train Loss: 0.004212, Test Loss: 0.004351
+Epoch: 042, Train Loss: 0.004132, Test Loss: 0.004276
+Epoch: 043, Train Loss: 0.004148, Test Loss: 0.004289
+Epoch: 044, Train Loss: 0.003967, Test Loss: 0.004098
+Epoch: 045, Train Loss: 0.003948, Test Loss: 0.004084
+Epoch: 046, Train Loss: 0.003984, Test Loss: 0.004108
+Epoch: 047, Train Loss: 0.003791, Test Loss: 0.003920
+Epoch: 048, Train Loss: 0.004121, Test Loss: 0.004241
+Epoch: 049, Train Loss: 0.003816, Test Loss: 0.003945

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG3SageDouble/model.py

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+import torch
+from torch.nn import Linear
+from torch_geometric.nn import TAGConv, SAGEConv, GraphConv, RGCNConv
+
+
+class StateModelEncoder(torch.nn.Module):
+    def __init__(self, hidden_channels, out_channels):
+        super().__init__()
+        self.conv1 = RGCNConv(5, hidden_channels, 3)
+        self.conv2 = TAGConv(hidden_channels, hidden_channels, 3)  # TAGConv
+        self.conv3 = GraphConv((-1, -1), hidden_channels)  # SAGEConv
+        self.conv32 = SAGEConv((-1, -1), hidden_channels)  # SAGEConv
+        self.conv4 = SAGEConv((-1, -1), hidden_channels)
+        self.conv42 = SAGEConv((-1, -1), hidden_channels)
+        self.conv5 = SAGEConv(-1, hidden_channels)
+        self.lin = Linear(hidden_channels, out_channels)
+
+    def forward(
+        self,
+        game_x,
+        state_x,
+        edge_index_v_v,
+        edge_type_v_v,
+        edge_index_history_v_s,
+        edge_attr_history_v_s,
+        edge_index_in_v_s,
+        edge_index_s_s,
+    ):
+        game_x = self.conv1(game_x, edge_index_v_v, edge_type_v_v).relu()
+
+        state_x = self.conv3(
+            (game_x, state_x),
+            edge_index_history_v_s,
+            edge_attr_history_v_s,
+        ).relu()
+
+        state_x = self.conv32(
+            (game_x, state_x),
+            edge_index_history_v_s,
+        ).relu()
+
+        state_x = self.conv4(
+            (game_x, state_x),
+            edge_index_in_v_s,
+        ).relu()
+
+        state_x = self.conv42(
+            (game_x, state_x),
+            edge_index_in_v_s,
+        ).relu()
+
+        state_x = self.conv2(
+            state_x,
+            edge_index_s_s,
+        ).relu()
+
+        state_x = self.conv5(
+            state_x,
+            edge_index_s_s,
+        ).relu()
+
+        return self.lin(state_x)

VSharp.ML.AIAgent/ml/models/TAGSageDouble/32ch/20e/train_res

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+Epoch: 000, Train Loss: 0.072378, Test Loss: 0.073496
+Epoch: 001, Train Loss: 0.043047, Test Loss: 0.043577
+Epoch: 002, Train Loss: 0.034224, Test Loss: 0.034591
+Epoch: 003, Train Loss: 0.028296, Test Loss: 0.028690
+Epoch: 004, Train Loss: 0.024289, Test Loss: 0.024659
+Epoch: 005, Train Loss: 0.021720, Test Loss: 0.022099
+Epoch: 006, Train Loss: 0.020279, Test Loss: 0.020652
+Epoch: 007, Train Loss: 0.018290, Test Loss: 0.018627
+Epoch: 008, Train Loss: 0.016991, Test Loss: 0.017269
+Epoch: 009, Train Loss: 0.016168, Test Loss: 0.016476
+Epoch: 010, Train Loss: 0.014912, Test Loss: 0.015189
+Epoch: 011, Train Loss: 0.014065, Test Loss: 0.014356
+Epoch: 012, Train Loss: 0.014232, Test Loss: 0.014502
+Epoch: 013, Train Loss: 0.013865, Test Loss: 0.014091
+Epoch: 014, Train Loss: 0.013500, Test Loss: 0.013771
+Epoch: 015, Train Loss: 0.012756, Test Loss: 0.012998
+Epoch: 016, Train Loss: 0.012388, Test Loss: 0.012617
+Epoch: 017, Train Loss: 0.012210, Test Loss: 0.012405
+Epoch: 018, Train Loss: 0.012160, Test Loss: 0.012321
+Epoch: 019, Train Loss: 0.011690, Test Loss: 0.011885

VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/100e/train_res

@@ -0,0 +1,100 @@
+Epoch: 000, Train Loss: 0.047441, Test Loss: 0.047307
+Epoch: 001, Train Loss: 0.027983, Test Loss: 0.028091
+Epoch: 002, Train Loss: 0.019875, Test Loss: 0.019800
+Epoch: 003, Train Loss: 0.015671, Test Loss: 0.015709
+Epoch: 004, Train Loss: 0.013007, Test Loss: 0.013053
+Epoch: 005, Train Loss: 0.010809, Test Loss: 0.010988
+Epoch: 006, Train Loss: 0.009938, Test Loss: 0.010142
+Epoch: 007, Train Loss: 0.009297, Test Loss: 0.009558
+Epoch: 008, Train Loss: 0.008306, Test Loss: 0.008476
+Epoch: 009, Train Loss: 0.007844, Test Loss: 0.008031
+Epoch: 010, Train Loss: 0.007373, Test Loss: 0.007566
+Epoch: 011, Train Loss: 0.007039, Test Loss: 0.007258
+Epoch: 012, Train Loss: 0.006610, Test Loss: 0.006775
+Epoch: 013, Train Loss: 0.006401, Test Loss: 0.006583
+Epoch: 014, Train Loss: 0.006156, Test Loss: 0.006349
+Epoch: 015, Train Loss: 0.006003, Test Loss: 0.006174
+Epoch: 016, Train Loss: 0.005762, Test Loss: 0.005955
+Epoch: 017, Train Loss: 0.005660, Test Loss: 0.005828
+Epoch: 018, Train Loss: 0.005280, Test Loss: 0.005431
+Epoch: 019, Train Loss: 0.005207, Test Loss: 0.005368
+Epoch: 020, Train Loss: 0.005102, Test Loss: 0.005245
+Epoch: 021, Train Loss: 0.004956, Test Loss: 0.005110
+Epoch: 022, Train Loss: 0.004837, Test Loss: 0.004973
+Epoch: 023, Train Loss: 0.004780, Test Loss: 0.004912
+Epoch: 024, Train Loss: 0.004628, Test Loss: 0.004775
+Epoch: 025, Train Loss: 0.004730, Test Loss: 0.004861
+Epoch: 026, Train Loss: 0.004443, Test Loss: 0.004571
+Epoch: 027, Train Loss: 0.004354, Test Loss: 0.004493
+Epoch: 028, Train Loss: 0.004315, Test Loss: 0.004458
+Epoch: 029, Train Loss: 0.004350, Test Loss: 0.004485
+Epoch: 030, Train Loss: 0.004211, Test Loss: 0.004352
+Epoch: 031, Train Loss: 0.004113, Test Loss: 0.004253
+Epoch: 032, Train Loss: 0.004068, Test Loss: 0.004211
+Epoch: 033, Train Loss: 0.004030, Test Loss: 0.004165
+Epoch: 034, Train Loss: 0.003899, Test Loss: 0.004036
+Epoch: 035, Train Loss: 0.003832, Test Loss: 0.003964
+Epoch: 036, Train Loss: 0.003761, Test Loss: 0.003910
+Epoch: 037, Train Loss: 0.003703, Test Loss: 0.003839
+Epoch: 038, Train Loss: 0.003704, Test Loss: 0.003837
+Epoch: 039, Train Loss: 0.003600, Test Loss: 0.003739
+Epoch: 040, Train Loss: 0.003544, Test Loss: 0.003683
+Epoch: 041, Train Loss: 0.003528, Test Loss: 0.003679
+Epoch: 042, Train Loss: 0.003515, Test Loss: 0.003656
+Epoch: 043, Train Loss: 0.003423, Test Loss: 0.003566
+Epoch: 044, Train Loss: 0.003475, Test Loss: 0.003613
+Epoch: 045, Train Loss: 0.003346, Test Loss: 0.003486
+Epoch: 046, Train Loss: 0.003336, Test Loss: 0.003475
+Epoch: 047, Train Loss: 0.003270, Test Loss: 0.003401
+Epoch: 048, Train Loss: 0.003265, Test Loss: 0.003391
+Epoch: 049, Train Loss: 0.003190, Test Loss: 0.003301
+Epoch: 050, Train Loss: 0.003140, Test Loss: 0.003256
+Epoch: 051, Train Loss: 0.003111, Test Loss: 0.003236
+Epoch: 052, Train Loss: 0.003114, Test Loss: 0.003231
+Epoch: 053, Train Loss: 0.003045, Test Loss: 0.003151
+Epoch: 054, Train Loss: 0.002983, Test Loss: 0.003090
+Epoch: 055, Train Loss: 0.002978, Test Loss: 0.003089
+Epoch: 056, Train Loss: 0.002931, Test Loss: 0.003043
+Epoch: 057, Train Loss: 0.002931, Test Loss: 0.003039
+Epoch: 058, Train Loss: 0.002922, Test Loss: 0.003025
+Epoch: 059, Train Loss: 0.002918, Test Loss: 0.003017
+Epoch: 060, Train Loss: 0.002894, Test Loss: 0.002988
+Epoch: 061, Train Loss: 0.002802, Test Loss: 0.002901
+Epoch: 062, Train Loss: 0.002828, Test Loss: 0.002917
+Epoch: 063, Train Loss: 0.002812, Test Loss: 0.002898
+Epoch: 064, Train Loss: 0.002941, Test Loss: 0.003026
+Epoch: 065, Train Loss: 0.002762, Test Loss: 0.002856
+Epoch: 066, Train Loss: 0.002782, Test Loss: 0.002879
+Epoch: 067, Train Loss: 0.002745, Test Loss: 0.002826
+Epoch: 068, Train Loss: 0.002705, Test Loss: 0.002787
+Epoch: 069, Train Loss: 0.002805, Test Loss: 0.002893
+Epoch: 070, Train Loss: 0.002758, Test Loss: 0.002843
+Epoch: 071, Train Loss: 0.002624, Test Loss: 0.002710
+Epoch: 072, Train Loss: 0.002774, Test Loss: 0.002854
+Epoch: 073, Train Loss: 0.002759, Test Loss: 0.002842
+Epoch: 074, Train Loss: 0.002614, Test Loss: 0.002701
+Epoch: 075, Train Loss: 0.002605, Test Loss: 0.002688
+Epoch: 076, Train Loss: 0.002582, Test Loss: 0.002659
+Epoch: 077, Train Loss: 0.002550, Test Loss: 0.002635
+Epoch: 078, Train Loss: 0.002603, Test Loss: 0.002696
+Epoch: 079, Train Loss: 0.002573, Test Loss: 0.002650
+Epoch: 080, Train Loss: 0.002495, Test Loss: 0.002565
+Epoch: 081, Train Loss: 0.002490, Test Loss: 0.002565
+Epoch: 082, Train Loss: 0.002456, Test Loss: 0.002534
+Epoch: 083, Train Loss: 0.002446, Test Loss: 0.002514
+Epoch: 084, Train Loss: 0.002489, Test Loss: 0.002568
+Epoch: 085, Train Loss: 0.002667, Test Loss: 0.002737
+Epoch: 086, Train Loss: 0.002421, Test Loss: 0.002483
+Epoch: 087, Train Loss: 0.002547, Test Loss: 0.002629
+Epoch: 088, Train Loss: 0.002356, Test Loss: 0.002422
+Epoch: 089, Train Loss: 0.002392, Test Loss: 0.002453
+Epoch: 090, Train Loss: 0.002351, Test Loss: 0.002427
+Epoch: 091, Train Loss: 0.002332, Test Loss: 0.002407
+Epoch: 092, Train Loss: 0.004105, Test Loss: 0.004236
+Epoch: 093, Train Loss: 0.002387, Test Loss: 0.002469
+Epoch: 094, Train Loss: 0.002316, Test Loss: 0.002390
+Epoch: 095, Train Loss: 0.002291, Test Loss: 0.002376
+Epoch: 096, Train Loss: 0.002354, Test Loss: 0.002421
+Epoch: 097, Train Loss: 0.002294, Test Loss: 0.002367
+Epoch: 098, Train Loss: 0.002244, Test Loss: 0.002338
+Epoch: 099, Train Loss: 0.002263, Test Loss: 0.002370

VSharp.ML.AIAgent/ml/models/TAGSageDouble/64ch/20e/train_res

@@ -0,0 +1,20 @@
+Epoch: 000, Train Loss: 0.058653, Test Loss: 0.061931
+Epoch: 001, Train Loss: 0.033599, Test Loss: 0.035252
+Epoch: 002, Train Loss: 0.024017, Test Loss: 0.025401
+Epoch: 003, Train Loss: 0.018566, Test Loss: 0.020084
+Epoch: 004, Train Loss: 0.016096, Test Loss: 0.017555
+Epoch: 005, Train Loss: 0.014193, Test Loss: 0.015381
+Epoch: 006, Train Loss: 0.012142, Test Loss: 0.013107
+Epoch: 007, Train Loss: 0.011092, Test Loss: 0.011963
+Epoch: 008, Train Loss: 0.010418, Test Loss: 0.011354
+Epoch: 009, Train Loss: 0.008393, Test Loss: 0.008903
+Epoch: 010, Train Loss: 0.008293, Test Loss: 0.008749
+Epoch: 011, Train Loss: 0.007354, Test Loss: 0.007718
+Epoch: 012, Train Loss: 0.006936, Test Loss: 0.007338
+Epoch: 013, Train Loss: 0.006520, Test Loss: 0.006797
+Epoch: 014, Train Loss: 0.006349, Test Loss: 0.006665
+Epoch: 015, Train Loss: 0.005991, Test Loss: 0.006300
+Epoch: 016, Train Loss: 0.005723, Test Loss: 0.006024
+Epoch: 017, Train Loss: 0.005562, Test Loss: 0.005868
+Epoch: 018, Train Loss: 0.005451, Test Loss: 0.005739
+Epoch: 019, Train Loss: 0.005401, Test Loss: 0.005666