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Merge pull request #78 from katyacyfra/mlSearcher
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Аdd some pretrained models
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emnigma authored Dec 5, 2023
2 parents 0df023e + 1a5ad07 commit 408a938
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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
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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
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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
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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
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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)
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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
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