Merge pull request #78 from katyacyfra/mlSearcher

Аdd some pretrained models

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG2VerticesDouble/32ch/50e/train_res

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+Epoch: 000, Train Loss: 0.059122, Test Loss: 0.059964
+Epoch: 001, Train Loss: 0.038855, Test Loss: 0.039430
+Epoch: 002, Train Loss: 0.029786, Test Loss: 0.030119
+Epoch: 003, Train Loss: 0.024270, Test Loss: 0.024509
+Epoch: 004, Train Loss: 0.020792, Test Loss: 0.021115
+Epoch: 005, Train Loss: 0.017929, Test Loss: 0.018149
+Epoch: 006, Train Loss: 0.016036, Test Loss: 0.016234
+Epoch: 007, Train Loss: 0.014971, Test Loss: 0.015116
+Epoch: 008, Train Loss: 0.013815, Test Loss: 0.013943
+Epoch: 009, Train Loss: 0.013231, Test Loss: 0.013315
+Epoch: 010, Train Loss: 0.012711, Test Loss: 0.012753
+Epoch: 011, Train Loss: 0.012124, Test Loss: 0.012133
+Epoch: 012, Train Loss: 0.011642, Test Loss: 0.011636
+Epoch: 013, Train Loss: 0.011475, Test Loss: 0.011482
+Epoch: 014, Train Loss: 0.011344, Test Loss: 0.011288
+Epoch: 015, Train Loss: 0.011121, Test Loss: 0.011025
+Epoch: 016, Train Loss: 0.010511, Test Loss: 0.010454
+Epoch: 017, Train Loss: 0.010048, Test Loss: 0.009998
+Epoch: 018, Train Loss: 0.010281, Test Loss: 0.010169
+Epoch: 019, Train Loss: 0.009415, Test Loss: 0.009375
+Epoch: 020, Train Loss: 0.009319, Test Loss: 0.009258
+Epoch: 021, Train Loss: 0.008570, Test Loss: 0.008602
+Epoch: 022, Train Loss: 0.008747, Test Loss: 0.008783
+Epoch: 023, Train Loss: 0.008152, Test Loss: 0.008256
+Epoch: 024, Train Loss: 0.007792, Test Loss: 0.007926
+Epoch: 025, Train Loss: 0.007487, Test Loss: 0.007645
+Epoch: 026, Train Loss: 0.007250, Test Loss: 0.007391
+Epoch: 027, Train Loss: 0.007021, Test Loss: 0.007208
+Epoch: 028, Train Loss: 0.006973, Test Loss: 0.007146
+Epoch: 029, Train Loss: 0.006621, Test Loss: 0.006818
+Epoch: 030, Train Loss: 0.006720, Test Loss: 0.006909
+Epoch: 031, Train Loss: 0.006614, Test Loss: 0.006824
+Epoch: 032, Train Loss: 0.006298, Test Loss: 0.006511
+Epoch: 033, Train Loss: 0.006266, Test Loss: 0.006468
+Epoch: 034, Train Loss: 0.006041, Test Loss: 0.006268
+Epoch: 035, Train Loss: 0.005902, Test Loss: 0.006126
+Epoch: 036, Train Loss: 0.005832, Test Loss: 0.006063
+Epoch: 037, Train Loss: 0.005818, Test Loss: 0.006040
+Epoch: 038, Train Loss: 0.005659, Test Loss: 0.005892
+Epoch: 039, Train Loss: 0.005553, Test Loss: 0.005800
+Epoch: 040, Train Loss: 0.005546, Test Loss: 0.005777
+Epoch: 041, Train Loss: 0.005445, Test Loss: 0.005681
+Epoch: 042, Train Loss: 0.005366, Test Loss: 0.005610
+Epoch: 043, Train Loss: 0.005320, Test Loss: 0.005567
+Epoch: 044, Train Loss: 0.005238, Test Loss: 0.005477
+Epoch: 045, Train Loss: 0.005240, Test Loss: 0.005475
+Epoch: 046, Train Loss: 0.005135, Test Loss: 0.005381
+Epoch: 047, Train Loss: 0.005087, Test Loss: 0.005332
+Epoch: 048, Train Loss: 0.005119, Test Loss: 0.005340
+Epoch: 049, Train Loss: 0.005116, Test Loss: 0.005339

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG2VerticesDouble/64ch/100e/train_res

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+Epoch: 000, Train Loss: 0.040080, Test Loss: 0.040366
+Epoch: 001, Train Loss: 0.020591, Test Loss: 0.020814
+Epoch: 002, Train Loss: 0.014610, Test Loss: 0.014798
+Epoch: 003, Train Loss: 0.013165, Test Loss: 0.013216
+Epoch: 004, Train Loss: 0.012829, Test Loss: 0.012804
+Epoch: 005, Train Loss: 0.011173, Test Loss: 0.011158
+Epoch: 006, Train Loss: 0.009944, Test Loss: 0.009942
+Epoch: 007, Train Loss: 0.010120, Test Loss: 0.010082
+Epoch: 008, Train Loss: 0.008894, Test Loss: 0.008864
+Epoch: 009, Train Loss: 0.008147, Test Loss: 0.008130
+Epoch: 010, Train Loss: 0.007288, Test Loss: 0.007290
+Epoch: 011, Train Loss: 0.006985, Test Loss: 0.006982
+Epoch: 012, Train Loss: 0.006598, Test Loss: 0.006611
+Epoch: 013, Train Loss: 0.006351, Test Loss: 0.006373
+Epoch: 014, Train Loss: 0.006065, Test Loss: 0.006075
+Epoch: 015, Train Loss: 0.006192, Test Loss: 0.006187
+Epoch: 016, Train Loss: 0.005840, Test Loss: 0.005839
+Epoch: 017, Train Loss: 0.005528, Test Loss: 0.005530
+Epoch: 018, Train Loss: 0.005721, Test Loss: 0.005723
+Epoch: 019, Train Loss: 0.005340, Test Loss: 0.005340
+Epoch: 020, Train Loss: 0.005146, Test Loss: 0.005162
+Epoch: 021, Train Loss: 0.005406, Test Loss: 0.005417
+Epoch: 022, Train Loss: 0.004950, Test Loss: 0.004966
+Epoch: 023, Train Loss: 0.005079, Test Loss: 0.005083
+Epoch: 024, Train Loss: 0.005161, Test Loss: 0.005192
+Epoch: 025, Train Loss: 0.004708, Test Loss: 0.004746
+Epoch: 026, Train Loss: 0.004645, Test Loss: 0.004679
+Epoch: 027, Train Loss: 0.004525, Test Loss: 0.004560
+Epoch: 028, Train Loss: 0.004408, Test Loss: 0.004456
+Epoch: 029, Train Loss: 0.004468, Test Loss: 0.004510
+Epoch: 030, Train Loss: 0.004402, Test Loss: 0.004468
+Epoch: 031, Train Loss: 0.004193, Test Loss: 0.004245
+Epoch: 032, Train Loss: 0.004118, Test Loss: 0.004159
+Epoch: 033, Train Loss: 0.003933, Test Loss: 0.003995
+Epoch: 034, Train Loss: 0.003687, Test Loss: 0.003753
+Epoch: 035, Train Loss: 0.003449, Test Loss: 0.003509
+Epoch: 036, Train Loss: 0.003361, Test Loss: 0.003421
+Epoch: 037, Train Loss: 0.003373, Test Loss: 0.003438
+Epoch: 038, Train Loss: 0.003399, Test Loss: 0.003460
+Epoch: 039, Train Loss: 0.003387, Test Loss: 0.003437
+Epoch: 040, Train Loss: 0.003195, Test Loss: 0.003243
+Epoch: 041, Train Loss: 0.003161, Test Loss: 0.003208
+Epoch: 042, Train Loss: 0.003043, Test Loss: 0.003097
+Epoch: 043, Train Loss: 0.003178, Test Loss: 0.003233
+Epoch: 044, Train Loss: 0.002973, Test Loss: 0.003005
+Epoch: 045, Train Loss: 0.002876, Test Loss: 0.002935
+Epoch: 046, Train Loss: 0.002953, Test Loss: 0.002999
+Epoch: 047, Train Loss: 0.002940, Test Loss: 0.003003
+Epoch: 048, Train Loss: 0.003112, Test Loss: 0.003177
+Epoch: 049, Train Loss: 0.003024, Test Loss: 0.003087
+Epoch: 050, Train Loss: 0.002853, Test Loss: 0.002906
+Epoch: 051, Train Loss: 0.002791, Test Loss: 0.002840
+Epoch: 052, Train Loss: 0.002684, Test Loss: 0.002733
+Epoch: 053, Train Loss: 0.002741, Test Loss: 0.002794
+Epoch: 054, Train Loss: 0.002821, Test Loss: 0.002864
+Epoch: 055, Train Loss: 0.002796, Test Loss: 0.002838
+Epoch: 056, Train Loss: 0.002629, Test Loss: 0.002672
+Epoch: 057, Train Loss: 0.002585, Test Loss: 0.002619
+Epoch: 058, Train Loss: 0.002692, Test Loss: 0.002742
+Epoch: 059, Train Loss: 0.002519, Test Loss: 0.002566
+Epoch: 060, Train Loss: 0.002548, Test Loss: 0.002582
+Epoch: 061, Train Loss: 0.002486, Test Loss: 0.002524
+Epoch: 062, Train Loss: 0.002639, Test Loss: 0.002685
+Epoch: 063, Train Loss: 0.002484, Test Loss: 0.002514
+Epoch: 064, Train Loss: 0.002483, Test Loss: 0.002531
+Epoch: 065, Train Loss: 0.002434, Test Loss: 0.002469
+Epoch: 066, Train Loss: 0.002418, Test Loss: 0.002445
+Epoch: 067, Train Loss: 0.002229, Test Loss: 0.002274
+Epoch: 068, Train Loss: 0.002398, Test Loss: 0.002430
+Epoch: 069, Train Loss: 0.002341, Test Loss: 0.002373
+Epoch: 070, Train Loss: 0.002470, Test Loss: 0.002496
+Epoch: 071, Train Loss: 0.002318, Test Loss: 0.002347
+Epoch: 072, Train Loss: 0.002284, Test Loss: 0.002307
+Epoch: 073, Train Loss: 0.002353, Test Loss: 0.002380
+Epoch: 074, Train Loss: 0.002370, Test Loss: 0.002403
+Epoch: 075, Train Loss: 0.002214, Test Loss: 0.002249
+Epoch: 076, Train Loss: 0.002246, Test Loss: 0.002271
+Epoch: 077, Train Loss: 0.002182, Test Loss: 0.002214
+Epoch: 078, Train Loss: 0.002171, Test Loss: 0.002194
+Epoch: 079, Train Loss: 0.002115, Test Loss: 0.002154
+Epoch: 080, Train Loss: 0.002119, Test Loss: 0.002142
+Epoch: 081, Train Loss: 0.002104, Test Loss: 0.002140
+Epoch: 082, Train Loss: 0.002072, Test Loss: 0.002086
+Epoch: 083, Train Loss: 0.002116, Test Loss: 0.002141
+Epoch: 084, Train Loss: 0.001982, Test Loss: 0.002020
+Epoch: 085, Train Loss: 0.002053, Test Loss: 0.002058
+Epoch: 086, Train Loss: 0.001931, Test Loss: 0.001964
+Epoch: 087, Train Loss: 0.001907, Test Loss: 0.001934
+Epoch: 088, Train Loss: 0.002038, Test Loss: 0.002052
+Epoch: 089, Train Loss: 0.001925, Test Loss: 0.001938
+Epoch: 090, Train Loss: 0.001974, Test Loss: 0.001997
+Epoch: 091, Train Loss: 0.002047, Test Loss: 0.002051
+Epoch: 092, Train Loss: 0.002117, Test Loss: 0.002124
+Epoch: 093, Train Loss: 0.001960, Test Loss: 0.001964
+Epoch: 094, Train Loss: 0.001901, Test Loss: 0.001919
+Epoch: 095, Train Loss: 0.001925, Test Loss: 0.001931
+Epoch: 096, Train Loss: 0.001824, Test Loss: 0.001844
+Epoch: 097, Train Loss: 0.001893, Test Loss: 0.001903
+Epoch: 098, Train Loss: 0.001913, Test Loss: 0.001923
+Epoch: 099, Train Loss: 0.001843, Test Loss: 0.001849

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG2VerticesDouble/64ch/20e/train_res

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+Epoch: 000, Train Loss: 0.038977, Test Loss: 0.038516
+Epoch: 001, Train Loss: 0.021613, Test Loss: 0.021252
+Epoch: 002, Train Loss: 0.015219, Test Loss: 0.014979
+Epoch: 003, Train Loss: 0.012758, Test Loss: 0.012641
+Epoch: 004, Train Loss: 0.010339, Test Loss: 0.010257
+Epoch: 005, Train Loss: 0.009468, Test Loss: 0.009407
+Epoch: 006, Train Loss: 0.008761, Test Loss: 0.008682
+Epoch: 007, Train Loss: 0.008724, Test Loss: 0.008638
+Epoch: 008, Train Loss: 0.007264, Test Loss: 0.007202
+Epoch: 009, Train Loss: 0.006791, Test Loss: 0.006737
+Epoch: 010, Train Loss: 0.006485, Test Loss: 0.006446
+Epoch: 011, Train Loss: 0.005976, Test Loss: 0.005945
+Epoch: 012, Train Loss: 0.006247, Test Loss: 0.006212
+Epoch: 013, Train Loss: 0.005417, Test Loss: 0.005384
+Epoch: 014, Train Loss: 0.005269, Test Loss: 0.005255
+Epoch: 015, Train Loss: 0.005017, Test Loss: 0.005002
+Epoch: 016, Train Loss: 0.005051, Test Loss: 0.005055
+Epoch: 017, Train Loss: 0.004882, Test Loss: 0.004887
+Epoch: 018, Train Loss: 0.004844, Test Loss: 0.004865
+Epoch: 019, Train Loss: 0.004383, Test Loss: 0.004402

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG2VerticesDouble/64ch/50e/train_res

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+Epoch: 000, Train Loss: 0.041597, Test Loss: 0.042314
+Epoch: 001, Train Loss: 0.022224, Test Loss: 0.022520
+Epoch: 002, Train Loss: 0.015402, Test Loss: 0.015499
+Epoch: 003, Train Loss: 0.012459, Test Loss: 0.012488
+Epoch: 004, Train Loss: 0.010423, Test Loss: 0.010482
+Epoch: 005, Train Loss: 0.009028, Test Loss: 0.009105
+Epoch: 006, Train Loss: 0.008365, Test Loss: 0.008432
+Epoch: 007, Train Loss: 0.007737, Test Loss: 0.007772
+Epoch: 008, Train Loss: 0.007703, Test Loss: 0.007679
+Epoch: 009, Train Loss: 0.007362, Test Loss: 0.007345
+Epoch: 010, Train Loss: 0.007206, Test Loss: 0.007160
+Epoch: 011, Train Loss: 0.007105, Test Loss: 0.007049
+Epoch: 012, Train Loss: 0.006998, Test Loss: 0.006952
+Epoch: 013, Train Loss: 0.006941, Test Loss: 0.006890
+Epoch: 014, Train Loss: 0.006803, Test Loss: 0.006776
+Epoch: 015, Train Loss: 0.006417, Test Loss: 0.006379
+Epoch: 016, Train Loss: 0.006711, Test Loss: 0.006704
+Epoch: 017, Train Loss: 0.006518, Test Loss: 0.006511
+Epoch: 018, Train Loss: 0.005905, Test Loss: 0.005912
+Epoch: 019, Train Loss: 0.005999, Test Loss: 0.005999
+Epoch: 020, Train Loss: 0.005870, Test Loss: 0.005876
+Epoch: 021, Train Loss: 0.005153, Test Loss: 0.005179
+Epoch: 022, Train Loss: 0.004757, Test Loss: 0.004792
+Epoch: 023, Train Loss: 0.004716, Test Loss: 0.004747
+Epoch: 024, Train Loss: 0.004693, Test Loss: 0.004728
+Epoch: 025, Train Loss: 0.004347, Test Loss: 0.004368
+Epoch: 026, Train Loss: 0.004345, Test Loss: 0.004373
+Epoch: 027, Train Loss: 0.004342, Test Loss: 0.004363
+Epoch: 028, Train Loss: 0.004469, Test Loss: 0.004503
+Epoch: 029, Train Loss: 0.004789, Test Loss: 0.004808
+Epoch: 030, Train Loss: 0.004646, Test Loss: 0.004667
+Epoch: 031, Train Loss: 0.004922, Test Loss: 0.004936
+Epoch: 032, Train Loss: 0.005485, Test Loss: 0.005505
+Epoch: 033, Train Loss: 0.005273, Test Loss: 0.005267
+Epoch: 034, Train Loss: 0.004670, Test Loss: 0.004688
+Epoch: 035, Train Loss: 0.004818, Test Loss: 0.004826
+Epoch: 036, Train Loss: 0.004808, Test Loss: 0.004823
+Epoch: 037, Train Loss: 0.003915, Test Loss: 0.003935
+Epoch: 038, Train Loss: 0.003706, Test Loss: 0.003749
+Epoch: 039, Train Loss: 0.003967, Test Loss: 0.003998
+Epoch: 040, Train Loss: 0.003534, Test Loss: 0.003591
+Epoch: 041, Train Loss: 0.003419, Test Loss: 0.003499
+Epoch: 042, Train Loss: 0.003418, Test Loss: 0.003476
+Epoch: 043, Train Loss: 0.003506, Test Loss: 0.003560
+Epoch: 044, Train Loss: 0.003293, Test Loss: 0.003342
+Epoch: 045, Train Loss: 0.003199, Test Loss: 0.003255
+Epoch: 046, Train Loss: 0.003239, Test Loss: 0.003297
+Epoch: 047, Train Loss: 0.003402, Test Loss: 0.003442
+Epoch: 048, Train Loss: 0.003318, Test Loss: 0.003360
+Epoch: 049, Train Loss: 0.003193, Test Loss: 0.003257

VSharp.ML.AIAgent/ml/models/RGCNEdgeTypeTAG2VerticesDouble/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.conv12 = TAGConv(hidden_channels, hidden_channels, 2)
+        self.conv2 = TAGConv(hidden_channels, hidden_channels, 3)
+        self.conv3 = GraphConv((-1, -1), hidden_channels)
+        self.conv32 = SAGEConv((-1, -1), hidden_channels)
+        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()
+
+        game_x = self.conv12(game_x, edge_index_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/RGCNEdgeTypeTAG3SageDouble/64ch/100e/train_res

@@ -0,0 +1,100 @@
+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