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Epoch: [ 0] [ 0/ 468] time: 0.2491, train_loss: 2.15030980, train_accuracy: 0.2266, test_Accuracy: 0.1452
Epoch: [ 0] [ 1/ 468] time: 0.3121, train_loss: 2.15283918, train_accuracy: 0.1953, test_Accuracy: 0.2136
Epoch: [ 0] [ 2/ 468] time: 0.3721, train_loss: 2.07774782, train_accuracy: 0.4297, test_Accuracy: 0.3395
Epoch: [ 0] [ 3/ 468] time: 0.4331, train_loss: 1.97704232, train_accuracy: 0.4609, test_Accuracy: 0.4211
Epoch: [ 0] [ 4/ 468] time: 0.4951, train_loss: 1.93319905, train_accuracy: 0.5078, test_Accuracy: 0.4982
Epoch: [ 0] [ 5/ 468] time: 0.5631, train_loss: 1.84458375, train_accuracy: 0.6172, test_Accuracy: 0.6005
Epoch: [ 0] [ 6/ 468] time: 0.6231, train_loss: 1.71073520, train_accuracy: 0.6875, test_Accuracy: 0.6867
Epoch: [ 0] [ 7/ 468] time: 0.6842, train_loss: 1.68754315, train_accuracy: 0.6719, test_Accuracy: 0.7173
Epoch: [ 0] [ 8/ 468] time: 0.7452, train_loss: 1.56382334, train_accuracy: 0.7188, test_Accuracy: 0.7309
Epoch: [ 0] [ 9/ 468] time: 0.8052, train_loss: 1.37600899, train_accuracy: 0.8203, test_Accuracy: 0.7405
Epoch: [ 0] [ 10/ 468] time: 0.8662, train_loss: 1.38046825, train_accuracy: 0.7422, test_Accuracy: 0.7595
Epoch: [ 0] [ 11/ 468] time: 0.9272, train_loss: 1.20876694, train_accuracy: 0.7812, test_Accuracy: 0.7675
Epoch: [ 0] [ 12/ 468] time: 0.9873, train_loss: 1.14961326, train_accuracy: 0.7500, test_Accuracy: 0.7821
Epoch: [ 0] [ 13/ 468] time: 1.0492, train_loss: 0.97968102, train_accuracy: 0.8047, test_Accuracy: 0.7916
Epoch: [ 0] [ 14/ 468] time: 1.1092, train_loss: 0.86035222, train_accuracy: 0.8359, test_Accuracy: 0.8006
Epoch: [ 0] [ 15/ 468] time: 1.1713, train_loss: 0.93435884, train_accuracy: 0.7578, test_Accuracy: 0.8078
Epoch: [ 0] [ 16/ 468] time: 1.2353, train_loss: 0.77967739, train_accuracy: 0.8203, test_Accuracy: 0.8119
Epoch: [ 0] [ 17/ 468] time: 1.2973, train_loss: 0.82329828, train_accuracy: 0.7969, test_Accuracy: 0.8164
Epoch: [ 0] [ 18/ 468] time: 1.3593, train_loss: 0.76127410, train_accuracy: 0.7969, test_Accuracy: 0.8252
Epoch: [ 0] [ 19/ 468] time: 1.4233, train_loss: 0.59374988, train_accuracy: 0.8828, test_Accuracy: 0.8308
Epoch: [ 0] [ 20/ 468] time: 1.4853, train_loss: 0.65207708, train_accuracy: 0.8359, test_Accuracy: 0.8344
Epoch: [ 0] [ 21/ 468] time: 1.5493, train_loss: 0.52844054, train_accuracy: 0.8750, test_Accuracy: 0.8334
Epoch: [ 0] [ 22/ 468] time: 1.6114, train_loss: 0.58252573, train_accuracy: 0.8359, test_Accuracy: 0.8299
Epoch: [ 0] [ 23/ 468] time: 1.6744, train_loss: 0.60676157, train_accuracy: 0.8438, test_Accuracy: 0.8308
Epoch: [ 0] [ 24/ 468] time: 1.7354, train_loss: 0.52588582, train_accuracy: 0.8828, test_Accuracy: 0.8374
Epoch: [ 0] [ 25/ 468] time: 1.7974, train_loss: 0.49769706, train_accuracy: 0.8672, test_Accuracy: 0.8474
Epoch: [ 0] [ 26/ 468] time: 1.8594, train_loss: 0.50299680, train_accuracy: 0.8906, test_Accuracy: 0.8379
Epoch: [ 0] [ 27/ 468] time: 1.9214, train_loss: 0.46636519, train_accuracy: 0.8594, test_Accuracy: 0.8283
Epoch: [ 0] [ 28/ 468] time: 1.9834, train_loss: 0.59428501, train_accuracy: 0.8281, test_Accuracy: 0.8398
Epoch: [ 0] [ 29/ 468] time: 2.0455, train_loss: 0.56251818, train_accuracy: 0.8047, test_Accuracy: 0.8509
Epoch: [ 0] [ 30/ 468] time: 2.1065, train_loss: 0.43280989, train_accuracy: 0.8672, test_Accuracy: 0.8555
Epoch: [ 0] [ 31/ 468] time: 2.1685, train_loss: 0.35328683, train_accuracy: 0.9062, test_Accuracy: 0.8549
Epoch: [ 0] [ 32/ 468] time: 2.2315, train_loss: 0.40768445, train_accuracy: 0.8594, test_Accuracy: 0.8494
Epoch: [ 0] [ 33/ 468] time: 2.2935, train_loss: 0.54843789, train_accuracy: 0.8125, test_Accuracy: 0.8529
Epoch: [ 0] [ 34/ 468] time: 2.3555, train_loss: 0.53448266, train_accuracy: 0.8281, test_Accuracy: 0.8615
Epoch: [ 0] [ 35/ 468] time: 2.4185, train_loss: 0.48472366, train_accuracy: 0.8594, test_Accuracy: 0.8612
Epoch: [ 0] [ 36/ 468] time: 2.4806, train_loss: 0.50503701, train_accuracy: 0.8594, test_Accuracy: 0.8586
Epoch: [ 0] [ 37/ 468] time: 2.5446, train_loss: 0.28531340, train_accuracy: 0.9297, test_Accuracy: 0.8637
Epoch: [ 0] [ 38/ 468] time: 2.6066, train_loss: 0.42061746, train_accuracy: 0.8594, test_Accuracy: 0.8762
Epoch: [ 0] [ 39/ 468] time: 2.6686, train_loss: 0.43485492, train_accuracy: 0.8750, test_Accuracy: 0.8860
Epoch: [ 0] [ 40/ 468] time: 2.7326, train_loss: 0.41276726, train_accuracy: 0.9062, test_Accuracy: 0.8844
Epoch: [ 0] [ 41/ 468] time: 2.7946, train_loss: 0.28081536, train_accuracy: 0.9062, test_Accuracy: 0.8801
Epoch: [ 0] [ 42/ 468] time: 2.8576, train_loss: 0.35974616, train_accuracy: 0.9141, test_Accuracy: 0.8688
Epoch: [ 0] [ 43/ 468] time: 2.9207, train_loss: 0.42074358, train_accuracy: 0.8594, test_Accuracy: 0.8673
Epoch: [ 0] [ 44/ 468] time: 2.9837, train_loss: 0.32754454, train_accuracy: 0.8828, test_Accuracy: 0.8779
Epoch: [ 0] [ 45/ 468] time: 3.0457, train_loss: 0.32231712, train_accuracy: 0.8828, test_Accuracy: 0.8874
Epoch: [ 0] [ 46/ 468] time: 3.1087, train_loss: 0.36304191, train_accuracy: 0.8984, test_Accuracy: 0.8924
Epoch: [ 0] [ 47/ 468] time: 3.1697, train_loss: 0.32422566, train_accuracy: 0.9141, test_Accuracy: 0.8952
Epoch: [ 0] [ 48/ 468] time: 3.2327, train_loss: 0.38969386, train_accuracy: 0.8906, test_Accuracy: 0.8958
Epoch: [ 0] [ 49/ 468] time: 3.2957, train_loss: 0.43795654, train_accuracy: 0.8672, test_Accuracy: 0.8888
Epoch: [ 0] [ 50/ 468] time: 3.3598, train_loss: 0.43280196, train_accuracy: 0.8906, test_Accuracy: 0.8884
Epoch: [ 0] [ 51/ 468] time: 3.4228, train_loss: 0.40492800, train_accuracy: 0.8750, test_Accuracy: 0.8937
Epoch: [ 0] [ 52/ 468] time: 3.4858, train_loss: 0.45982653, train_accuracy: 0.8594, test_Accuracy: 0.8952
Epoch: [ 0] [ 53/ 468] time: 3.5468, train_loss: 0.32028058, train_accuracy: 0.8828, test_Accuracy: 0.8982
Epoch: [ 0] [ 54/ 468] time: 3.6078, train_loss: 0.31702724, train_accuracy: 0.8906, test_Accuracy: 0.8973
Epoch: [ 0] [ 55/ 468] time: 3.6708, train_loss: 0.41682231, train_accuracy: 0.8906, test_Accuracy: 0.8983
Epoch: [ 0] [ 56/ 468] time: 3.7339, train_loss: 0.21412303, train_accuracy: 0.9453, test_Accuracy: 0.8946
Epoch: [ 0] [ 57/ 468] time: 3.7969, train_loss: 0.46382612, train_accuracy: 0.8828, test_Accuracy: 0.8953
Epoch: [ 0] [ 58/ 468] time: 3.8609, train_loss: 0.27687752, train_accuracy: 0.8984, test_Accuracy: 0.8997
Epoch: [ 0] [ 59/ 468] time: 3.9239, train_loss: 0.27421039, train_accuracy: 0.9609, test_Accuracy: 0.9016
Epoch: [ 0] [ 60/ 468] time: 3.9869, train_loss: 0.37226164, train_accuracy: 0.8672, test_Accuracy: 0.8985
Epoch: [ 0] [ 61/ 468] time: 4.0499, train_loss: 0.29157472, train_accuracy: 0.9062, test_Accuracy: 0.8959
Epoch: [ 0] [ 62/ 468] time: 4.1129, train_loss: 0.26518056, train_accuracy: 0.9141, test_Accuracy: 0.8958
Epoch: [ 0] [ 63/ 468] time: 4.1780, train_loss: 0.49583787, train_accuracy: 0.8906, test_Accuracy: 0.8961
Epoch: [ 0] [ 64/ 468] time: 4.2420, train_loss: 0.26262233, train_accuracy: 0.9453, test_Accuracy: 0.9020
Epoch: [ 0] [ 65/ 468] time: 4.3060, train_loss: 0.38248271, train_accuracy: 0.8906, test_Accuracy: 0.9087
Epoch: [ 0] [ 66/ 468] time: 4.3691, train_loss: 0.25547937, train_accuracy: 0.8984, test_Accuracy: 0.9130
Epoch: [ 0] [ 67/ 468] time: 4.4331, train_loss: 0.37517202, train_accuracy: 0.9062, test_Accuracy: 0.9101
Epoch: [ 0] [ 68/ 468] time: 4.4951, train_loss: 0.24114588, train_accuracy: 0.9453, test_Accuracy: 0.9071
Epoch: [ 0] [ 69/ 468] time: 4.5591, train_loss: 0.30137047, train_accuracy: 0.9297, test_Accuracy: 0.9033
Epoch: [ 0] [ 70/ 468] time: 4.6231, train_loss: 0.35740495, train_accuracy: 0.9297, test_Accuracy: 0.9020
Epoch: [ 0] [ 71/ 468] time: 4.6841, train_loss: 0.41990116, train_accuracy: 0.8750, test_Accuracy: 0.9031
Epoch: [ 0] [ 72/ 468] time: 4.7461, train_loss: 0.32718772, train_accuracy: 0.9062, test_Accuracy: 0.9058
Epoch: [ 0] [ 73/ 468] time: 4.8092, train_loss: 0.32029492, train_accuracy: 0.9141, test_Accuracy: 0.9101
Epoch: [ 0] [ 74/ 468] time: 4.8702, train_loss: 0.34653026, train_accuracy: 0.8906, test_Accuracy: 0.9116
Epoch: [ 0] [ 75/ 468] time: 4.9342, train_loss: 0.24824965, train_accuracy: 0.9219, test_Accuracy: 0.9108
Epoch: [ 0] [ 76/ 468] time: 4.9972, train_loss: 0.39011461, train_accuracy: 0.9062, test_Accuracy: 0.9096
Epoch: [ 0] [ 77/ 468] time: 5.0612, train_loss: 0.36081627, train_accuracy: 0.9062, test_Accuracy: 0.9024
Epoch: [ 0] [ 78/ 468] time: 5.1252, train_loss: 0.32710829, train_accuracy: 0.8906, test_Accuracy: 0.9033
Epoch: [ 0] [ 79/ 468] time: 5.1892, train_loss: 0.30211586, train_accuracy: 0.9297, test_Accuracy: 0.9091
Epoch: [ 0] [ 80/ 468] time: 5.2543, train_loss: 0.26078090, train_accuracy: 0.9141, test_Accuracy: 0.9107
Epoch: [ 0] [ 81/ 468] time: 5.3173, train_loss: 0.30378014, train_accuracy: 0.8984, test_Accuracy: 0.9113
Epoch: [ 0] [ 82/ 468] time: 5.3803, train_loss: 0.36620122, train_accuracy: 0.8984, test_Accuracy: 0.9108
Epoch: [ 0] [ 83/ 468] time: 5.4433, train_loss: 0.32149518, train_accuracy: 0.9062, test_Accuracy: 0.9101
Epoch: [ 0] [ 84/ 468] time: 5.5093, train_loss: 0.29505837, train_accuracy: 0.9375, test_Accuracy: 0.9065
Epoch: [ 0] [ 85/ 468] time: 5.5703, train_loss: 0.33091930, train_accuracy: 0.8906, test_Accuracy: 0.9053
Epoch: [ 0] [ 86/ 468] time: 5.6333, train_loss: 0.38630185, train_accuracy: 0.9141, test_Accuracy: 0.9068
Epoch: [ 0] [ 87/ 468] time: 5.6984, train_loss: 0.41085005, train_accuracy: 0.8984, test_Accuracy: 0.9038
Epoch: [ 0] [ 88/ 468] time: 5.7624, train_loss: 0.31273714, train_accuracy: 0.8984, test_Accuracy: 0.9055
Epoch: [ 0] [ 89/ 468] time: 5.8244, train_loss: 0.29829884, train_accuracy: 0.9062, test_Accuracy: 0.9007
Epoch: [ 0] [ 90/ 468] time: 5.8884, train_loss: 0.42691422, train_accuracy: 0.8750, test_Accuracy: 0.9044
Epoch: [ 0] [ 91/ 468] time: 5.9544, train_loss: 0.19773099, train_accuracy: 0.9609, test_Accuracy: 0.9092
Epoch: [ 0] [ 92/ 468] time: 6.0164, train_loss: 0.33233923, train_accuracy: 0.9062, test_Accuracy: 0.9121
Epoch: [ 0] [ 93/ 468] time: 6.0804, train_loss: 0.29973486, train_accuracy: 0.8906, test_Accuracy: 0.9118
Epoch: [ 0] [ 94/ 468] time: 6.1455, train_loss: 0.35997713, train_accuracy: 0.8594, test_Accuracy: 0.9134
Epoch: [ 0] [ 95/ 468] time: 6.2085, train_loss: 0.26744440, train_accuracy: 0.9297, test_Accuracy: 0.9142
Epoch: [ 0] [ 96/ 468] time: 6.2715, train_loss: 0.30835310, train_accuracy: 0.8828, test_Accuracy: 0.9148
Epoch: [ 0] [ 97/ 468] time: 6.3365, train_loss: 0.41458651, train_accuracy: 0.9062, test_Accuracy: 0.9150
Epoch: [ 0] [ 98/ 468] time: 6.3995, train_loss: 0.25687534, train_accuracy: 0.9453, test_Accuracy: 0.9163
Epoch: [ 0] [ 99/ 468] time: 6.4635, train_loss: 0.35696569, train_accuracy: 0.9062, test_Accuracy: 0.9199
Epoch: [ 0] [ 100/ 468] time: 6.5275, train_loss: 0.31090885, train_accuracy: 0.9141, test_Accuracy: 0.9179
Epoch: [ 0] [ 101/ 468] time: 6.5906, train_loss: 0.26249218, train_accuracy: 0.9297, test_Accuracy: 0.9162
Epoch: [ 0] [ 102/ 468] time: 6.6561, train_loss: 0.21557218, train_accuracy: 0.9297, test_Accuracy: 0.9161
Epoch: [ 0] [ 103/ 468] time: 6.7241, train_loss: 0.26813257, train_accuracy: 0.9297, test_Accuracy: 0.9177
Epoch: [ 0] [ 104/ 468] time: 6.7921, train_loss: 0.26840457, train_accuracy: 0.9297, test_Accuracy: 0.9204
Epoch: [ 0] [ 105/ 468] time: 6.8581, train_loss: 0.41396719, train_accuracy: 0.8906, test_Accuracy: 0.9244
Epoch: [ 0] [ 106/ 468] time: 6.9231, train_loss: 0.20383561, train_accuracy: 0.9297, test_Accuracy: 0.9254
Epoch: [ 0] [ 107/ 468] time: 6.9891, train_loss: 0.19787546, train_accuracy: 0.9531, test_Accuracy: 0.9237
Epoch: [ 0] [ 108/ 468] time: 7.0551, train_loss: 0.34419316, train_accuracy: 0.8828, test_Accuracy: 0.9234
Epoch: [ 0] [ 109/ 468] time: 7.1212, train_loss: 0.25148118, train_accuracy: 0.9062, test_Accuracy: 0.9208
Epoch: [ 0] [ 110/ 468] time: 7.1912, train_loss: 0.27769178, train_accuracy: 0.9219, test_Accuracy: 0.9171
Epoch: [ 0] [ 111/ 468] time: 7.2572, train_loss: 0.28824270, train_accuracy: 0.9375, test_Accuracy: 0.9185
Epoch: [ 0] [ 112/ 468] time: 7.3232, train_loss: 0.31092465, train_accuracy: 0.9219, test_Accuracy: 0.9225
Epoch: [ 0] [ 113/ 468] time: 7.3892, train_loss: 0.29452521, train_accuracy: 0.9219, test_Accuracy: 0.9233
Epoch: [ 0] [ 114/ 468] time: 7.4562, train_loss: 0.27070722, train_accuracy: 0.9297, test_Accuracy: 0.9252
Epoch: [ 0] [ 115/ 468] time: 7.5223, train_loss: 0.32723838, train_accuracy: 0.9297, test_Accuracy: 0.9234
Epoch: [ 0] [ 116/ 468] time: 7.5863, train_loss: 0.20157896, train_accuracy: 0.9453, test_Accuracy: 0.9200
Epoch: [ 0] [ 117/ 468] time: 7.6533, train_loss: 0.22456610, train_accuracy: 0.9609, test_Accuracy: 0.9177
Epoch: [ 0] [ 118/ 468] time: 7.7173, train_loss: 0.22926557, train_accuracy: 0.8984, test_Accuracy: 0.9195
Epoch: [ 0] [ 119/ 468] time: 7.7813, train_loss: 0.25986317, train_accuracy: 0.9219, test_Accuracy: 0.9240
Epoch: [ 0] [ 120/ 468] time: 7.8463, train_loss: 0.33479416, train_accuracy: 0.9297, test_Accuracy: 0.9245
Epoch: [ 0] [ 121/ 468] time: 7.9123, train_loss: 0.20577163, train_accuracy: 0.9297, test_Accuracy: 0.9252
Epoch: [ 0] [ 122/ 468] time: 7.9774, train_loss: 0.28843778, train_accuracy: 0.9062, test_Accuracy: 0.9246
Epoch: [ 0] [ 123/ 468] time: 8.0434, train_loss: 0.23792754, train_accuracy: 0.9375, test_Accuracy: 0.9240
Epoch: [ 0] [ 124/ 468] time: 8.1084, train_loss: 0.23528665, train_accuracy: 0.9141, test_Accuracy: 0.9243
Epoch: [ 0] [ 125/ 468] time: 8.1724, train_loss: 0.31796750, train_accuracy: 0.8984, test_Accuracy: 0.9254
Epoch: [ 0] [ 126/ 468] time: 8.2354, train_loss: 0.19401328, train_accuracy: 0.9219, test_Accuracy: 0.9265
Epoch: [ 0] [ 127/ 468] time: 8.3004, train_loss: 0.16888312, train_accuracy: 0.9453, test_Accuracy: 0.9243
Epoch: [ 0] [ 128/ 468] time: 8.3644, train_loss: 0.32847032, train_accuracy: 0.8984, test_Accuracy: 0.9222
Epoch: [ 0] [ 129/ 468] time: 8.4295, train_loss: 0.27693975, train_accuracy: 0.8906, test_Accuracy: 0.9219
Epoch: [ 0] [ 130/ 468] time: 8.4945, train_loss: 0.22807607, train_accuracy: 0.9375, test_Accuracy: 0.9209
Epoch: [ 0] [ 131/ 468] time: 8.5595, train_loss: 0.22568117, train_accuracy: 0.9375, test_Accuracy: 0.9244
Epoch: [ 0] [ 132/ 468] time: 8.6225, train_loss: 0.27173108, train_accuracy: 0.9062, test_Accuracy: 0.9284
Epoch: [ 0] [ 133/ 468] time: 8.6865, train_loss: 0.35024145, train_accuracy: 0.8906, test_Accuracy: 0.9275
Epoch: [ 0] [ 134/ 468] time: 8.7495, train_loss: 0.38954973, train_accuracy: 0.8984, test_Accuracy: 0.9271
Epoch: [ 0] [ 135/ 468] time: 8.8135, train_loss: 0.21493477, train_accuracy: 0.9453, test_Accuracy: 0.9241
Epoch: [ 0] [ 136/ 468] time: 8.8786, train_loss: 0.25806636, train_accuracy: 0.9297, test_Accuracy: 0.9189
Epoch: [ 0] [ 137/ 468] time: 8.9446, train_loss: 0.20212270, train_accuracy: 0.9219, test_Accuracy: 0.9154
Epoch: [ 0] [ 138/ 468] time: 9.0096, train_loss: 0.28960535, train_accuracy: 0.9297, test_Accuracy: 0.9127
Epoch: [ 0] [ 139/ 468] time: 9.0726, train_loss: 0.35245126, train_accuracy: 0.9297, test_Accuracy: 0.9151
Epoch: [ 0] [ 140/ 468] time: 9.1386, train_loss: 0.26913369, train_accuracy: 0.9219, test_Accuracy: 0.9212
Epoch: [ 0] [ 141/ 468] time: 9.2026, train_loss: 0.27163938, train_accuracy: 0.9141, test_Accuracy: 0.9264
Epoch: [ 0] [ 142/ 468] time: 9.2716, train_loss: 0.22377852, train_accuracy: 0.9453, test_Accuracy: 0.9282
Epoch: [ 0] [ 143/ 468] time: 9.3377, train_loss: 0.27024600, train_accuracy: 0.9297, test_Accuracy: 0.9295
Epoch: [ 0] [ 144/ 468] time: 9.4077, train_loss: 0.29181483, train_accuracy: 0.9219, test_Accuracy: 0.9280
Epoch: [ 0] [ 145/ 468] time: 9.4727, train_loss: 0.36190426, train_accuracy: 0.8906, test_Accuracy: 0.9266
Epoch: [ 0] [ 146/ 468] time: 9.5367, train_loss: 0.24922608, train_accuracy: 0.9531, test_Accuracy: 0.9274
Epoch: [ 0] [ 147/ 468] time: 9.6007, train_loss: 0.32412627, train_accuracy: 0.8906, test_Accuracy: 0.9272
Epoch: [ 0] [ 148/ 468] time: 9.6667, train_loss: 0.30410588, train_accuracy: 0.9375, test_Accuracy: 0.9282
Epoch: [ 0] [ 149/ 468] time: 9.7358, train_loss: 0.26427433, train_accuracy: 0.9297, test_Accuracy: 0.9270
Epoch: [ 0] [ 150/ 468] time: 9.7998, train_loss: 0.30568987, train_accuracy: 0.8828, test_Accuracy: 0.9293
Epoch: [ 0] [ 151/ 468] time: 9.8678, train_loss: 0.26532823, train_accuracy: 0.9219, test_Accuracy: 0.9342
Epoch: [ 0] [ 152/ 468] time: 9.9348, train_loss: 0.29068148, train_accuracy: 0.9141, test_Accuracy: 0.9331
Epoch: [ 0] [ 153/ 468] time: 10.0028, train_loss: 0.23632655, train_accuracy: 0.9062, test_Accuracy: 0.9335
Epoch: [ 0] [ 154/ 468] time: 10.0688, train_loss: 0.25320745, train_accuracy: 0.9141, test_Accuracy: 0.9335
Epoch: [ 0] [ 155/ 468] time: 10.1358, train_loss: 0.22654940, train_accuracy: 0.9297, test_Accuracy: 0.9322
Epoch: [ 0] [ 156/ 468] time: 10.2039, train_loss: 0.23808193, train_accuracy: 0.9531, test_Accuracy: 0.9322
Epoch: [ 0] [ 157/ 468] time: 10.2719, train_loss: 0.24162428, train_accuracy: 0.9219, test_Accuracy: 0.9319
Epoch: [ 0] [ 158/ 468] time: 10.3429, train_loss: 0.23989542, train_accuracy: 0.9219, test_Accuracy: 0.9321
Epoch: [ 0] [ 159/ 468] time: 10.4099, train_loss: 0.20225845, train_accuracy: 0.9609, test_Accuracy: 0.9344
Epoch: [ 0] [ 160/ 468] time: 10.4769, train_loss: 0.23110092, train_accuracy: 0.9219, test_Accuracy: 0.9349
Epoch: [ 0] [ 161/ 468] time: 10.5449, train_loss: 0.21751849, train_accuracy: 0.9375, test_Accuracy: 0.9339
Epoch: [ 0] [ 162/ 468] time: 10.6090, train_loss: 0.16106503, train_accuracy: 0.9375, test_Accuracy: 0.9329
Epoch: [ 0] [ 163/ 468] time: 10.6740, train_loss: 0.20251328, train_accuracy: 0.9219, test_Accuracy: 0.9310
Epoch: [ 0] [ 164/ 468] time: 10.7390, train_loss: 0.23731238, train_accuracy: 0.9062, test_Accuracy: 0.9310
Epoch: [ 0] [ 165/ 468] time: 10.8030, train_loss: 0.22041874, train_accuracy: 0.9297, test_Accuracy: 0.9310
Epoch: [ 0] [ 166/ 468] time: 10.8670, train_loss: 0.27926773, train_accuracy: 0.9219, test_Accuracy: 0.9344
Epoch: [ 0] [ 167/ 468] time: 10.9310, train_loss: 0.20776446, train_accuracy: 0.9453, test_Accuracy: 0.9344
Epoch: [ 0] [ 168/ 468] time: 10.9940, train_loss: 0.16684905, train_accuracy: 0.9609, test_Accuracy: 0.9354
Epoch: [ 0] [ 169/ 468] time: 11.0601, train_loss: 0.17609364, train_accuracy: 0.9453, test_Accuracy: 0.9369
Epoch: [ 0] [ 170/ 468] time: 11.1241, train_loss: 0.23581663, train_accuracy: 0.9219, test_Accuracy: 0.9365
Epoch: [ 0] [ 171/ 468] time: 11.1891, train_loss: 0.15646684, train_accuracy: 0.9688, test_Accuracy: 0.9345
Epoch: [ 0] [ 172/ 468] time: 11.2541, train_loss: 0.31185722, train_accuracy: 0.9219, test_Accuracy: 0.9351
Epoch: [ 0] [ 173/ 468] time: 11.3191, train_loss: 0.22194964, train_accuracy: 0.9297, test_Accuracy: 0.9371
Epoch: [ 0] [ 174/ 468] time: 11.3821, train_loss: 0.17540474, train_accuracy: 0.9531, test_Accuracy: 0.9374
Epoch: [ 0] [ 175/ 468] time: 11.4471, train_loss: 0.30563429, train_accuracy: 0.8906, test_Accuracy: 0.9379
Epoch: [ 0] [ 176/ 468] time: 11.5142, train_loss: 0.18680054, train_accuracy: 0.9609, test_Accuracy: 0.9371
Epoch: [ 0] [ 177/ 468] time: 11.5782, train_loss: 0.18710050, train_accuracy: 0.9453, test_Accuracy: 0.9376
Epoch: [ 0] [ 178/ 468] time: 11.6412, train_loss: 0.14796190, train_accuracy: 0.9609, test_Accuracy: 0.9345
Epoch: [ 0] [ 179/ 468] time: 11.7042, train_loss: 0.21705720, train_accuracy: 0.9375, test_Accuracy: 0.9326
Epoch: [ 0] [ 180/ 468] time: 11.7682, train_loss: 0.20004642, train_accuracy: 0.9531, test_Accuracy: 0.9308
Epoch: [ 0] [ 181/ 468] time: 11.8292, train_loss: 0.18277654, train_accuracy: 0.9375, test_Accuracy: 0.9317
Epoch: [ 0] [ 182/ 468] time: 11.8932, train_loss: 0.23364887, train_accuracy: 0.9219, test_Accuracy: 0.9354
Epoch: [ 0] [ 183/ 468] time: 11.9563, train_loss: 0.18390165, train_accuracy: 0.9375, test_Accuracy: 0.9385
Epoch: [ 0] [ 184/ 468] time: 12.0203, train_loss: 0.18731409, train_accuracy: 0.9609, test_Accuracy: 0.9387
Epoch: [ 0] [ 185/ 468] time: 12.0833, train_loss: 0.13293701, train_accuracy: 0.9688, test_Accuracy: 0.9367
Epoch: [ 0] [ 186/ 468] time: 12.1453, train_loss: 0.26704201, train_accuracy: 0.9219, test_Accuracy: 0.9331
Epoch: [ 0] [ 187/ 468] time: 12.2093, train_loss: 0.30581164, train_accuracy: 0.9141, test_Accuracy: 0.9358
Epoch: [ 0] [ 188/ 468] time: 12.2723, train_loss: 0.26988789, train_accuracy: 0.8984, test_Accuracy: 0.9365
Epoch: [ 0] [ 189/ 468] time: 12.3363, train_loss: 0.28147525, train_accuracy: 0.9297, test_Accuracy: 0.9356
Epoch: [ 0] [ 190/ 468] time: 12.4014, train_loss: 0.20998138, train_accuracy: 0.9688, test_Accuracy: 0.9353
Epoch: [ 0] [ 191/ 468] time: 12.4654, train_loss: 0.16531554, train_accuracy: 0.9453, test_Accuracy: 0.9355
Epoch: [ 0] [ 192/ 468] time: 12.5284, train_loss: 0.16638854, train_accuracy: 0.9766, test_Accuracy: 0.9364
Epoch: [ 0] [ 193/ 468] time: 12.5914, train_loss: 0.14850360, train_accuracy: 0.9609, test_Accuracy: 0.9376
Epoch: [ 0] [ 194/ 468] time: 12.6544, train_loss: 0.30568868, train_accuracy: 0.9062, test_Accuracy: 0.9387
Epoch: [ 0] [ 195/ 468] time: 12.7184, train_loss: 0.12627041, train_accuracy: 0.9609, test_Accuracy: 0.9414
Epoch: [ 0] [ 196/ 468] time: 12.7825, train_loss: 0.23984389, train_accuracy: 0.9609, test_Accuracy: 0.9422
Epoch: [ 0] [ 197/ 468] time: 12.8475, train_loss: 0.16382484, train_accuracy: 0.9531, test_Accuracy: 0.9436
Epoch: [ 0] [ 198/ 468] time: 12.9115, train_loss: 0.12727252, train_accuracy: 0.9688, test_Accuracy: 0.9436
Epoch: [ 0] [ 199/ 468] time: 12.9756, train_loss: 0.24766417, train_accuracy: 0.9297, test_Accuracy: 0.9425
Epoch: [ 0] [ 200/ 468] time: 13.0385, train_loss: 0.24216126, train_accuracy: 0.9375, test_Accuracy: 0.9402
Epoch: [ 0] [ 201/ 468] time: 13.1025, train_loss: 0.19451016, train_accuracy: 0.9375, test_Accuracy: 0.9380
Epoch: [ 0] [ 202/ 468] time: 13.1655, train_loss: 0.09552706, train_accuracy: 0.9688, test_Accuracy: 0.9388
Epoch: [ 0] [ 203/ 468] time: 13.2286, train_loss: 0.20676467, train_accuracy: 0.9219, test_Accuracy: 0.9388
Epoch: [ 0] [ 204/ 468] time: 13.2916, train_loss: 0.16558582, train_accuracy: 0.9453, test_Accuracy: 0.9411
Epoch: [ 0] [ 205/ 468] time: 13.3556, train_loss: 0.17059493, train_accuracy: 0.9531, test_Accuracy: 0.9411
Epoch: [ 0] [ 206/ 468] time: 13.4206, train_loss: 0.11008885, train_accuracy: 0.9609, test_Accuracy: 0.9413
Epoch: [ 0] [ 207/ 468] time: 13.4846, train_loss: 0.15926999, train_accuracy: 0.9531, test_Accuracy: 0.9399
Epoch: [ 0] [ 208/ 468] time: 13.5477, train_loss: 0.26672536, train_accuracy: 0.9219, test_Accuracy: 0.9396
Epoch: [ 0] [ 209/ 468] time: 13.6117, train_loss: 0.23134579, train_accuracy: 0.9375, test_Accuracy: 0.9401
Epoch: [ 0] [ 210/ 468] time: 13.6748, train_loss: 0.15418190, train_accuracy: 0.9453, test_Accuracy: 0.9397
Epoch: [ 0] [ 211/ 468] time: 13.7388, train_loss: 0.18166092, train_accuracy: 0.9375, test_Accuracy: 0.9410
Epoch: [ 0] [ 212/ 468] time: 13.8028, train_loss: 0.20516403, train_accuracy: 0.9219, test_Accuracy: 0.9426
Epoch: [ 0] [ 213/ 468] time: 13.8688, train_loss: 0.21677539, train_accuracy: 0.9219, test_Accuracy: 0.9442
Epoch: [ 0] [ 214/ 468] time: 13.9348, train_loss: 0.22261241, train_accuracy: 0.9375, test_Accuracy: 0.9463
Epoch: [ 0] [ 215/ 468] time: 13.9988, train_loss: 0.34383842, train_accuracy: 0.8828, test_Accuracy: 0.9467
Epoch: [ 0] [ 216/ 468] time: 14.0658, train_loss: 0.23152712, train_accuracy: 0.9219, test_Accuracy: 0.9456
Epoch: [ 0] [ 217/ 468] time: 14.1299, train_loss: 0.21360737, train_accuracy: 0.9453, test_Accuracy: 0.9440
Epoch: [ 0] [ 218/ 468] time: 14.1959, train_loss: 0.14919339, train_accuracy: 0.9609, test_Accuracy: 0.9421
Epoch: [ 0] [ 219/ 468] time: 14.2629, train_loss: 0.09273322, train_accuracy: 0.9766, test_Accuracy: 0.9408
Epoch: [ 0] [ 220/ 468] time: 14.3319, train_loss: 0.15447523, train_accuracy: 0.9531, test_Accuracy: 0.9409
Epoch: [ 0] [ 221/ 468] time: 14.3979, train_loss: 0.27789184, train_accuracy: 0.9141, test_Accuracy: 0.9410
Epoch: [ 0] [ 222/ 468] time: 14.4629, train_loss: 0.12793493, train_accuracy: 0.9609, test_Accuracy: 0.9424
Epoch: [ 0] [ 223/ 468] time: 14.5269, train_loss: 0.12226766, train_accuracy: 0.9766, test_Accuracy: 0.9422
Epoch: [ 0] [ 224/ 468] time: 14.5910, train_loss: 0.13145107, train_accuracy: 0.9688, test_Accuracy: 0.9421
Epoch: [ 0] [ 225/ 468] time: 14.6580, train_loss: 0.17955813, train_accuracy: 0.9531, test_Accuracy: 0.9405
Epoch: [ 0] [ 226/ 468] time: 14.7220, train_loss: 0.22709191, train_accuracy: 0.9297, test_Accuracy: 0.9407
Epoch: [ 0] [ 227/ 468] time: 14.7860, train_loss: 0.22195145, train_accuracy: 0.9531, test_Accuracy: 0.9405
Epoch: [ 0] [ 228/ 468] time: 14.8490, train_loss: 0.19860703, train_accuracy: 0.9453, test_Accuracy: 0.9406
Epoch: [ 0] [ 229/ 468] time: 14.9150, train_loss: 0.20411161, train_accuracy: 0.9219, test_Accuracy: 0.9423
Epoch: [ 0] [ 230/ 468] time: 14.9800, train_loss: 0.17807995, train_accuracy: 0.9297, test_Accuracy: 0.9430
Epoch: [ 0] [ 231/ 468] time: 15.0431, train_loss: 0.16782898, train_accuracy: 0.9453, test_Accuracy: 0.9440
Epoch: [ 0] [ 232/ 468] time: 15.1071, train_loss: 0.08167590, train_accuracy: 0.9844, test_Accuracy: 0.9449
Epoch: [ 0] [ 233/ 468] time: 15.1701, train_loss: 0.17822459, train_accuracy: 0.9375, test_Accuracy: 0.9439
Epoch: [ 0] [ 234/ 468] time: 15.2331, train_loss: 0.22350088, train_accuracy: 0.9219, test_Accuracy: 0.9419
Epoch: [ 0] [ 235/ 468] time: 15.2981, train_loss: 0.15869054, train_accuracy: 0.9531, test_Accuracy: 0.9411
Epoch: [ 0] [ 236/ 468] time: 15.3631, train_loss: 0.06859242, train_accuracy: 0.9766, test_Accuracy: 0.9419
Epoch: [ 0] [ 237/ 468] time: 15.4251, train_loss: 0.30197757, train_accuracy: 0.8984, test_Accuracy: 0.9438
Epoch: [ 0] [ 238/ 468] time: 15.4902, train_loss: 0.11942769, train_accuracy: 0.9688, test_Accuracy: 0.9457
Epoch: [ 0] [ 239/ 468] time: 15.5532, train_loss: 0.15499094, train_accuracy: 0.9609, test_Accuracy: 0.9465
Epoch: [ 0] [ 240/ 468] time: 15.6162, train_loss: 0.23184153, train_accuracy: 0.9062, test_Accuracy: 0.9455
Epoch: [ 0] [ 241/ 468] time: 15.6802, train_loss: 0.24996555, train_accuracy: 0.9375, test_Accuracy: 0.9450
Epoch: [ 0] [ 242/ 468] time: 15.7462, train_loss: 0.11802086, train_accuracy: 0.9531, test_Accuracy: 0.9456
Epoch: [ 0] [ 243/ 468] time: 15.8092, train_loss: 0.26565617, train_accuracy: 0.9297, test_Accuracy: 0.9463
Epoch: [ 0] [ 244/ 468] time: 15.8733, train_loss: 0.14965780, train_accuracy: 0.9531, test_Accuracy: 0.9442
Epoch: [ 0] [ 245/ 468] time: 15.9403, train_loss: 0.18698113, train_accuracy: 0.9375, test_Accuracy: 0.9439
Epoch: [ 0] [ 246/ 468] time: 16.0043, train_loss: 0.15558021, train_accuracy: 0.9531, test_Accuracy: 0.9433
Epoch: [ 0] [ 247/ 468] time: 16.0703, train_loss: 0.14589940, train_accuracy: 0.9531, test_Accuracy: 0.9439
Epoch: [ 0] [ 248/ 468] time: 16.1383, train_loss: 0.18045065, train_accuracy: 0.9375, test_Accuracy: 0.9416
Epoch: [ 0] [ 249/ 468] time: 16.2023, train_loss: 0.18498233, train_accuracy: 0.9375, test_Accuracy: 0.9415
Epoch: [ 0] [ 250/ 468] time: 16.2663, train_loss: 0.23034607, train_accuracy: 0.9297, test_Accuracy: 0.9418
Epoch: [ 0] [ 251/ 468] time: 16.3344, train_loss: 0.10552325, train_accuracy: 0.9688, test_Accuracy: 0.9418
Epoch: [ 0] [ 252/ 468] time: 16.4004, train_loss: 0.17797375, train_accuracy: 0.9688, test_Accuracy: 0.9433
Epoch: [ 0] [ 253/ 468] time: 16.4654, train_loss: 0.11630102, train_accuracy: 0.9688, test_Accuracy: 0.9450
Epoch: [ 0] [ 254/ 468] time: 16.5294, train_loss: 0.14214271, train_accuracy: 0.9297, test_Accuracy: 0.9455
Epoch: [ 0] [ 255/ 468] time: 16.5914, train_loss: 0.09587899, train_accuracy: 0.9766, test_Accuracy: 0.9453
Epoch: [ 0] [ 256/ 468] time: 16.6564, train_loss: 0.11949618, train_accuracy: 0.9688, test_Accuracy: 0.9406
Epoch: [ 0] [ 257/ 468] time: 16.7214, train_loss: 0.19924688, train_accuracy: 0.9219, test_Accuracy: 0.9391
Epoch: [ 0] [ 258/ 468] time: 16.7845, train_loss: 0.15476713, train_accuracy: 0.9531, test_Accuracy: 0.9396
Epoch: [ 0] [ 259/ 468] time: 16.8485, train_loss: 0.13927916, train_accuracy: 0.9688, test_Accuracy: 0.9433
Epoch: [ 0] [ 260/ 468] time: 16.9125, train_loss: 0.11039710, train_accuracy: 0.9688, test_Accuracy: 0.9464
Epoch: [ 0] [ 261/ 468] time: 16.9745, train_loss: 0.28463781, train_accuracy: 0.9219, test_Accuracy: 0.9484
Epoch: [ 0] [ 262/ 468] time: 17.0385, train_loss: 0.19300835, train_accuracy: 0.9531, test_Accuracy: 0.9499
Epoch: [ 0] [ 263/ 468] time: 17.1015, train_loss: 0.17742682, train_accuracy: 0.9531, test_Accuracy: 0.9480
Epoch: [ 0] [ 264/ 468] time: 17.1635, train_loss: 0.11956368, train_accuracy: 0.9531, test_Accuracy: 0.9458
Epoch: [ 0] [ 265/ 468] time: 17.2256, train_loss: 0.10494197, train_accuracy: 0.9688, test_Accuracy: 0.9435
Epoch: [ 0] [ 266/ 468] time: 17.2896, train_loss: 0.14761403, train_accuracy: 0.9531, test_Accuracy: 0.9434
Epoch: [ 0] [ 267/ 468] time: 17.3516, train_loss: 0.13441488, train_accuracy: 0.9609, test_Accuracy: 0.9451
Epoch: [ 0] [ 268/ 468] time: 17.4136, train_loss: 0.11155730, train_accuracy: 0.9922, test_Accuracy: 0.9481
Epoch: [ 0] [ 269/ 468] time: 17.4756, train_loss: 0.19391273, train_accuracy: 0.9688, test_Accuracy: 0.9492
Epoch: [ 0] [ 270/ 468] time: 17.5386, train_loss: 0.26175904, train_accuracy: 0.9375, test_Accuracy: 0.9490
Epoch: [ 0] [ 271/ 468] time: 17.6016, train_loss: 0.18650766, train_accuracy: 0.9297, test_Accuracy: 0.9487
Epoch: [ 0] [ 272/ 468] time: 17.6667, train_loss: 0.17990604, train_accuracy: 0.9375, test_Accuracy: 0.9469
Epoch: [ 0] [ 273/ 468] time: 17.7317, train_loss: 0.12978739, train_accuracy: 0.9688, test_Accuracy: 0.9459
Epoch: [ 0] [ 274/ 468] time: 17.7957, train_loss: 0.08045278, train_accuracy: 0.9922, test_Accuracy: 0.9446
Epoch: [ 0] [ 275/ 468] time: 17.8587, train_loss: 0.13658679, train_accuracy: 0.9688, test_Accuracy: 0.9462
Epoch: [ 0] [ 276/ 468] time: 17.9267, train_loss: 0.10277054, train_accuracy: 0.9766, test_Accuracy: 0.9473
Epoch: [ 0] [ 277/ 468] time: 17.9897, train_loss: 0.15788171, train_accuracy: 0.9766, test_Accuracy: 0.9491
Epoch: [ 0] [ 278/ 468] time: 18.0548, train_loss: 0.19351265, train_accuracy: 0.9062, test_Accuracy: 0.9494
Epoch: [ 0] [ 279/ 468] time: 18.1228, train_loss: 0.21694133, train_accuracy: 0.9062, test_Accuracy: 0.9513
Epoch: [ 0] [ 280/ 468] time: 18.1898, train_loss: 0.33667937, train_accuracy: 0.9297, test_Accuracy: 0.9521
Epoch: [ 0] [ 281/ 468] time: 18.2548, train_loss: 0.15434639, train_accuracy: 0.9531, test_Accuracy: 0.9510
Epoch: [ 0] [ 282/ 468] time: 18.3228, train_loss: 0.11569065, train_accuracy: 0.9531, test_Accuracy: 0.9508
Epoch: [ 0] [ 283/ 468] time: 18.3888, train_loss: 0.14032760, train_accuracy: 0.9531, test_Accuracy: 0.9518
Epoch: [ 0] [ 284/ 468] time: 18.4558, train_loss: 0.18152231, train_accuracy: 0.9297, test_Accuracy: 0.9503
Epoch: [ 0] [ 285/ 468] time: 18.5209, train_loss: 0.09862983, train_accuracy: 0.9766, test_Accuracy: 0.9504
Epoch: [ 0] [ 286/ 468] time: 18.5919, train_loss: 0.12200639, train_accuracy: 0.9688, test_Accuracy: 0.9474
Epoch: [ 0] [ 287/ 468] time: 18.6559, train_loss: 0.22918737, train_accuracy: 0.9141, test_Accuracy: 0.9476
Epoch: [ 0] [ 288/ 468] time: 18.7239, train_loss: 0.19751920, train_accuracy: 0.9375, test_Accuracy: 0.9502
Epoch: [ 0] [ 289/ 468] time: 18.7899, train_loss: 0.28085297, train_accuracy: 0.9141, test_Accuracy: 0.9508
Epoch: [ 0] [ 290/ 468] time: 18.8539, train_loss: 0.10131221, train_accuracy: 0.9609, test_Accuracy: 0.9522
Epoch: [ 0] [ 291/ 468] time: 18.9180, train_loss: 0.19732203, train_accuracy: 0.9219, test_Accuracy: 0.9529
Epoch: [ 0] [ 292/ 468] time: 18.9840, train_loss: 0.07863627, train_accuracy: 0.9844, test_Accuracy: 0.9527
Epoch: [ 0] [ 293/ 468] time: 19.0480, train_loss: 0.15197501, train_accuracy: 0.9531, test_Accuracy: 0.9524
Epoch: [ 0] [ 294/ 468] time: 19.1100, train_loss: 0.16639140, train_accuracy: 0.9609, test_Accuracy: 0.9526
Epoch: [ 0] [ 295/ 468] time: 19.1750, train_loss: 0.18607783, train_accuracy: 0.9297, test_Accuracy: 0.9523
Epoch: [ 0] [ 296/ 468] time: 19.2390, train_loss: 0.16796342, train_accuracy: 0.9531, test_Accuracy: 0.9523
Epoch: [ 0] [ 297/ 468] time: 19.3020, train_loss: 0.17327395, train_accuracy: 0.9375, test_Accuracy: 0.9519
Epoch: [ 0] [ 298/ 468] time: 19.3651, train_loss: 0.16989313, train_accuracy: 0.9609, test_Accuracy: 0.9518
Epoch: [ 0] [ 299/ 468] time: 19.4281, train_loss: 0.21625620, train_accuracy: 0.9297, test_Accuracy: 0.9520
Epoch: [ 0] [ 300/ 468] time: 19.4911, train_loss: 0.29546732, train_accuracy: 0.9297, test_Accuracy: 0.9531
Epoch: [ 0] [ 301/ 468] time: 19.5561, train_loss: 0.14657137, train_accuracy: 0.9531, test_Accuracy: 0.9540
Epoch: [ 0] [ 302/ 468] time: 19.6191, train_loss: 0.20857713, train_accuracy: 0.9219, test_Accuracy: 0.9521
Epoch: [ 0] [ 303/ 468] time: 19.6811, train_loss: 0.25434366, train_accuracy: 0.9297, test_Accuracy: 0.9509
Epoch: [ 0] [ 304/ 468] time: 19.7441, train_loss: 0.11656755, train_accuracy: 0.9531, test_Accuracy: 0.9489
Epoch: [ 0] [ 305/ 468] time: 19.8082, train_loss: 0.11812200, train_accuracy: 0.9609, test_Accuracy: 0.9468
Epoch: [ 0] [ 306/ 468] time: 19.8702, train_loss: 0.21424091, train_accuracy: 0.9297, test_Accuracy: 0.9451
Epoch: [ 0] [ 307/ 468] time: 19.9332, train_loss: 0.12282729, train_accuracy: 0.9609, test_Accuracy: 0.9451
Epoch: [ 0] [ 308/ 468] time: 19.9952, train_loss: 0.25347343, train_accuracy: 0.9297, test_Accuracy: 0.9472
Epoch: [ 0] [ 309/ 468] time: 20.0562, train_loss: 0.12584005, train_accuracy: 0.9766, test_Accuracy: 0.9504
Epoch: [ 0] [ 310/ 468] time: 20.1192, train_loss: 0.17315902, train_accuracy: 0.9375, test_Accuracy: 0.9515
Epoch: [ 0] [ 311/ 468] time: 20.1812, train_loss: 0.12967509, train_accuracy: 0.9531, test_Accuracy: 0.9527
Epoch: [ 0] [ 312/ 468] time: 20.2463, train_loss: 0.16925472, train_accuracy: 0.9531, test_Accuracy: 0.9510
Epoch: [ 0] [ 313/ 468] time: 20.3103, train_loss: 0.15002504, train_accuracy: 0.9531, test_Accuracy: 0.9493
Epoch: [ 0] [ 314/ 468] time: 20.3763, train_loss: 0.08000503, train_accuracy: 0.9766, test_Accuracy: 0.9465
Epoch: [ 0] [ 315/ 468] time: 20.4403, train_loss: 0.17883195, train_accuracy: 0.9297, test_Accuracy: 0.9474
Epoch: [ 0] [ 316/ 468] time: 20.5043, train_loss: 0.20756245, train_accuracy: 0.9453, test_Accuracy: 0.9502
Epoch: [ 0] [ 317/ 468] time: 20.5683, train_loss: 0.17249253, train_accuracy: 0.9297, test_Accuracy: 0.9516
Epoch: [ 0] [ 318/ 468] time: 20.6313, train_loss: 0.13240860, train_accuracy: 0.9609, test_Accuracy: 0.9509
Epoch: [ 0] [ 319/ 468] time: 20.6954, train_loss: 0.18395954, train_accuracy: 0.9375, test_Accuracy: 0.9494
Epoch: [ 0] [ 320/ 468] time: 20.7594, train_loss: 0.16948792, train_accuracy: 0.9688, test_Accuracy: 0.9466
Epoch: [ 0] [ 321/ 468] time: 20.8224, train_loss: 0.17623082, train_accuracy: 0.9531, test_Accuracy: 0.9446
Epoch: [ 0] [ 322/ 468] time: 20.8864, train_loss: 0.17252052, train_accuracy: 0.9453, test_Accuracy: 0.9455
Epoch: [ 0] [ 323/ 468] time: 20.9504, train_loss: 0.12580900, train_accuracy: 0.9609, test_Accuracy: 0.9466
Epoch: [ 0] [ 324/ 468] time: 21.0126, train_loss: 0.24108915, train_accuracy: 0.9219, test_Accuracy: 0.9508
Epoch: [ 0] [ 325/ 468] time: 21.0784, train_loss: 0.13873923, train_accuracy: 0.9453, test_Accuracy: 0.9524
Epoch: [ 0] [ 326/ 468] time: 21.1445, train_loss: 0.13623059, train_accuracy: 0.9688, test_Accuracy: 0.9529
Epoch: [ 0] [ 327/ 468] time: 21.2095, train_loss: 0.10226237, train_accuracy: 0.9766, test_Accuracy: 0.9510
Epoch: [ 0] [ 328/ 468] time: 21.2740, train_loss: 0.19152004, train_accuracy: 0.9609, test_Accuracy: 0.9482
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Epoch: [ 0] [ 330/ 468] time: 21.4020, train_loss: 0.18879429, train_accuracy: 0.9297, test_Accuracy: 0.9478
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Epoch: [ 0] [ 333/ 468] time: 21.6530, train_loss: 0.15576802, train_accuracy: 0.9375, test_Accuracy: 0.9546
Epoch: [ 0] [ 334/ 468] time: 21.7180, train_loss: 0.16457088, train_accuracy: 0.9531, test_Accuracy: 0.9550
Epoch: [ 0] [ 335/ 468] time: 21.7820, train_loss: 0.14703712, train_accuracy: 0.9609, test_Accuracy: 0.9538
Epoch: [ 0] [ 336/ 468] time: 21.8461, train_loss: 0.13901797, train_accuracy: 0.9531, test_Accuracy: 0.9540
Epoch: [ 0] [ 337/ 468] time: 21.9111, train_loss: 0.15841904, train_accuracy: 0.9609, test_Accuracy: 0.9540
Epoch: [ 0] [ 338/ 468] time: 21.9751, train_loss: 0.08693589, train_accuracy: 0.9688, test_Accuracy: 0.9548
Epoch: [ 0] [ 339/ 468] time: 22.0391, train_loss: 0.12024122, train_accuracy: 0.9766, test_Accuracy: 0.9547
Epoch: [ 0] [ 340/ 468] time: 22.1041, train_loss: 0.18121222, train_accuracy: 0.9531, test_Accuracy: 0.9552
Epoch: [ 0] [ 341/ 468] time: 22.1701, train_loss: 0.20300639, train_accuracy: 0.9531, test_Accuracy: 0.9556
Epoch: [ 0] [ 342/ 468] time: 22.2331, train_loss: 0.16562158, train_accuracy: 0.9609, test_Accuracy: 0.9552
Epoch: [ 0] [ 343/ 468] time: 22.2972, train_loss: 0.18433744, train_accuracy: 0.9375, test_Accuracy: 0.9565
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Epoch: [ 0] [ 345/ 468] time: 22.4252, train_loss: 0.29687661, train_accuracy: 0.9062, test_Accuracy: 0.9583
Epoch: [ 0] [ 346/ 468] time: 22.4902, train_loss: 0.13536343, train_accuracy: 0.9609, test_Accuracy: 0.9577
Epoch: [ 0] [ 347/ 468] time: 22.5542, train_loss: 0.16808861, train_accuracy: 0.9453, test_Accuracy: 0.9580
Epoch: [ 0] [ 348/ 468] time: 22.6182, train_loss: 0.13764171, train_accuracy: 0.9844, test_Accuracy: 0.9577
Epoch: [ 0] [ 349/ 468] time: 22.6833, train_loss: 0.11232210, train_accuracy: 0.9609, test_Accuracy: 0.9564
Epoch: [ 0] [ 350/ 468] time: 22.7463, train_loss: 0.14690028, train_accuracy: 0.9375, test_Accuracy: 0.9558
Epoch: [ 0] [ 351/ 468] time: 22.8113, train_loss: 0.17780462, train_accuracy: 0.9531, test_Accuracy: 0.9556
Epoch: [ 0] [ 352/ 468] time: 22.8753, train_loss: 0.14793049, train_accuracy: 0.9609, test_Accuracy: 0.9550
Epoch: [ 0] [ 353/ 468] time: 22.9393, train_loss: 0.20168084, train_accuracy: 0.9531, test_Accuracy: 0.9547
Epoch: [ 0] [ 354/ 468] time: 23.0033, train_loss: 0.14828789, train_accuracy: 0.9453, test_Accuracy: 0.9543
Epoch: [ 0] [ 355/ 468] time: 23.0663, train_loss: 0.20324868, train_accuracy: 0.9531, test_Accuracy: 0.9555
Epoch: [ 0] [ 356/ 468] time: 23.1294, train_loss: 0.15619661, train_accuracy: 0.9609, test_Accuracy: 0.9560
Epoch: [ 0] [ 357/ 468] time: 23.1924, train_loss: 0.20183887, train_accuracy: 0.9375, test_Accuracy: 0.9569
Epoch: [ 0] [ 358/ 468] time: 23.2574, train_loss: 0.15836586, train_accuracy: 0.9609, test_Accuracy: 0.9571
Epoch: [ 0] [ 359/ 468] time: 23.3214, train_loss: 0.16267470, train_accuracy: 0.9453, test_Accuracy: 0.9584
Epoch: [ 0] [ 360/ 468] time: 23.3864, train_loss: 0.13085663, train_accuracy: 0.9609, test_Accuracy: 0.9578
Epoch: [ 0] [ 361/ 468] time: 23.4504, train_loss: 0.18066928, train_accuracy: 0.9453, test_Accuracy: 0.9572
Epoch: [ 0] [ 362/ 468] time: 23.5141, train_loss: 0.20114744, train_accuracy: 0.9297, test_Accuracy: 0.9573
Epoch: [ 0] [ 363/ 468] time: 23.5755, train_loss: 0.11035044, train_accuracy: 0.9688, test_Accuracy: 0.9565
Epoch: [ 0] [ 364/ 468] time: 23.6385, train_loss: 0.14055173, train_accuracy: 0.9531, test_Accuracy: 0.9570
Epoch: [ 0] [ 365/ 468] time: 23.7016, train_loss: 0.15765198, train_accuracy: 0.9688, test_Accuracy: 0.9576
Epoch: [ 0] [ 366/ 468] time: 23.7646, train_loss: 0.14929019, train_accuracy: 0.9531, test_Accuracy: 0.9586
Epoch: [ 0] [ 367/ 468] time: 23.8300, train_loss: 0.28184396, train_accuracy: 0.9219, test_Accuracy: 0.9601
Epoch: [ 0] [ 368/ 468] time: 23.8940, train_loss: 0.12710188, train_accuracy: 0.9531, test_Accuracy: 0.9597
Epoch: [ 0] [ 369/ 468] time: 23.9570, train_loss: 0.07625520, train_accuracy: 0.9922, test_Accuracy: 0.9592
Epoch: [ 0] [ 370/ 468] time: 24.0250, train_loss: 0.10960338, train_accuracy: 0.9688, test_Accuracy: 0.9588
Epoch: [ 0] [ 371/ 468] time: 24.0890, train_loss: 0.08890978, train_accuracy: 0.9766, test_Accuracy: 0.9581
Epoch: [ 0] [ 372/ 468] time: 24.1520, train_loss: 0.07581397, train_accuracy: 0.9844, test_Accuracy: 0.9579
Epoch: [ 0] [ 373/ 468] time: 24.2180, train_loss: 0.15715739, train_accuracy: 0.9688, test_Accuracy: 0.9586
Epoch: [ 0] [ 374/ 468] time: 24.2815, train_loss: 0.09676296, train_accuracy: 0.9844, test_Accuracy: 0.9600
Epoch: [ 0] [ 375/ 468] time: 24.3465, train_loss: 0.11426444, train_accuracy: 0.9688, test_Accuracy: 0.9601
Epoch: [ 0] [ 376/ 468] time: 24.4115, train_loss: 0.19789585, train_accuracy: 0.9375, test_Accuracy: 0.9598
Epoch: [ 0] [ 377/ 468] time: 24.4755, train_loss: 0.13910045, train_accuracy: 0.9531, test_Accuracy: 0.9587
Epoch: [ 0] [ 378/ 468] time: 24.5420, train_loss: 0.10982578, train_accuracy: 0.9609, test_Accuracy: 0.9579
Epoch: [ 0] [ 379/ 468] time: 24.6084, train_loss: 0.11757764, train_accuracy: 0.9844, test_Accuracy: 0.9561
Epoch: [ 0] [ 380/ 468] time: 24.6744, train_loss: 0.11057130, train_accuracy: 0.9609, test_Accuracy: 0.9529
Epoch: [ 0] [ 381/ 468] time: 24.7404, train_loss: 0.13472016, train_accuracy: 0.9531, test_Accuracy: 0.9529
Epoch: [ 0] [ 382/ 468] time: 24.8054, train_loss: 0.14099713, train_accuracy: 0.9609, test_Accuracy: 0.9538
Epoch: [ 0] [ 383/ 468] time: 24.8704, train_loss: 0.13744125, train_accuracy: 0.9688, test_Accuracy: 0.9554
Epoch: [ 0] [ 384/ 468] time: 24.9348, train_loss: 0.21594217, train_accuracy: 0.9453, test_Accuracy: 0.9560
Epoch: [ 0] [ 385/ 468] time: 24.9998, train_loss: 0.11624073, train_accuracy: 0.9531, test_Accuracy: 0.9576
Epoch: [ 0] [ 386/ 468] time: 25.0639, train_loss: 0.11062561, train_accuracy: 0.9688, test_Accuracy: 0.9578
Epoch: [ 0] [ 387/ 468] time: 25.1289, train_loss: 0.08605760, train_accuracy: 0.9609, test_Accuracy: 0.9577
Epoch: [ 0] [ 388/ 468] time: 25.1929, train_loss: 0.06960788, train_accuracy: 0.9766, test_Accuracy: 0.9568
Epoch: [ 0] [ 389/ 468] time: 25.2579, train_loss: 0.14723164, train_accuracy: 0.9531, test_Accuracy: 0.9564
Epoch: [ 0] [ 390/ 468] time: 25.3219, train_loss: 0.17202045, train_accuracy: 0.9453, test_Accuracy: 0.9560
Epoch: [ 0] [ 391/ 468] time: 25.3869, train_loss: 0.13020836, train_accuracy: 0.9609, test_Accuracy: 0.9567
Epoch: [ 0] [ 392/ 468] time: 25.4510, train_loss: 0.18430941, train_accuracy: 0.9375, test_Accuracy: 0.9561
Epoch: [ 0] [ 393/ 468] time: 25.5155, train_loss: 0.11469187, train_accuracy: 0.9609, test_Accuracy: 0.9557
Epoch: [ 0] [ 394/ 468] time: 25.5794, train_loss: 0.11584131, train_accuracy: 0.9609, test_Accuracy: 0.9563
Epoch: [ 0] [ 395/ 468] time: 25.6516, train_loss: 0.23650636, train_accuracy: 0.9375, test_Accuracy: 0.9564
Epoch: [ 0] [ 396/ 468] time: 25.7157, train_loss: 0.13211471, train_accuracy: 0.9609, test_Accuracy: 0.9569
Epoch: [ 0] [ 397/ 468] time: 25.7784, train_loss: 0.09262250, train_accuracy: 0.9766, test_Accuracy: 0.9564
Epoch: [ 0] [ 398/ 468] time: 25.8424, train_loss: 0.17458144, train_accuracy: 0.9375, test_Accuracy: 0.9574
Epoch: [ 0] [ 399/ 468] time: 25.9054, train_loss: 0.15859000, train_accuracy: 0.9453, test_Accuracy: 0.9573
Epoch: [ 0] [ 400/ 468] time: 25.9704, train_loss: 0.15582328, train_accuracy: 0.9609, test_Accuracy: 0.9587
Epoch: [ 0] [ 401/ 468] time: 26.0325, train_loss: 0.05083877, train_accuracy: 0.9922, test_Accuracy: 0.9591
Epoch: [ 0] [ 402/ 468] time: 26.0955, train_loss: 0.19545192, train_accuracy: 0.9375, test_Accuracy: 0.9592
Epoch: [ 0] [ 403/ 468] time: 26.1575, train_loss: 0.18975025, train_accuracy: 0.9297, test_Accuracy: 0.9600
Epoch: [ 0] [ 404/ 468] time: 26.2205, train_loss: 0.13589118, train_accuracy: 0.9609, test_Accuracy: 0.9603
Epoch: [ 0] [ 405/ 468] time: 26.2845, train_loss: 0.21268882, train_accuracy: 0.9609, test_Accuracy: 0.9585
Epoch: [ 0] [ 406/ 468] time: 26.3475, train_loss: 0.14337090, train_accuracy: 0.9609, test_Accuracy: 0.9566
Epoch: [ 0] [ 407/ 468] time: 26.4105, train_loss: 0.14414740, train_accuracy: 0.9609, test_Accuracy: 0.9554
Epoch: [ 0] [ 408/ 468] time: 26.4766, train_loss: 0.13706176, train_accuracy: 0.9609, test_Accuracy: 0.9536
Epoch: [ 0] [ 409/ 468] time: 26.5396, train_loss: 0.13669115, train_accuracy: 0.9531, test_Accuracy: 0.9512
Epoch: [ 0] [ 410/ 468] time: 26.6056, train_loss: 0.15882169, train_accuracy: 0.9531, test_Accuracy: 0.9516
Epoch: [ 0] [ 411/ 468] time: 26.6686, train_loss: 0.07023047, train_accuracy: 0.9844, test_Accuracy: 0.9521
Epoch: [ 0] [ 412/ 468] time: 26.7316, train_loss: 0.08542548, train_accuracy: 0.9688, test_Accuracy: 0.9531
Epoch: [ 0] [ 413/ 468] time: 26.7956, train_loss: 0.26154473, train_accuracy: 0.9141, test_Accuracy: 0.9557
Epoch: [ 0] [ 414/ 468] time: 26.8587, train_loss: 0.14225608, train_accuracy: 0.9531, test_Accuracy: 0.9558
Epoch: [ 0] [ 415/ 468] time: 26.9237, train_loss: 0.13583456, train_accuracy: 0.9297, test_Accuracy: 0.9546
Epoch: [ 0] [ 416/ 468] time: 26.9877, train_loss: 0.07992653, train_accuracy: 0.9766, test_Accuracy: 0.9523
Epoch: [ 0] [ 417/ 468] time: 27.0517, train_loss: 0.17846315, train_accuracy: 0.9297, test_Accuracy: 0.9511
Epoch: [ 0] [ 418/ 468] time: 27.1147, train_loss: 0.15516707, train_accuracy: 0.9375, test_Accuracy: 0.9499
Epoch: [ 0] [ 419/ 468] time: 27.1787, train_loss: 0.13926333, train_accuracy: 0.9531, test_Accuracy: 0.9514
Epoch: [ 0] [ 420/ 468] time: 27.2417, train_loss: 0.11705200, train_accuracy: 0.9531, test_Accuracy: 0.9552
Epoch: [ 0] [ 421/ 468] time: 27.3058, train_loss: 0.16251163, train_accuracy: 0.9453, test_Accuracy: 0.9580
Epoch: [ 0] [ 422/ 468] time: 27.3678, train_loss: 0.15031728, train_accuracy: 0.9453, test_Accuracy: 0.9588
Epoch: [ 0] [ 423/ 468] time: 27.4298, train_loss: 0.13261396, train_accuracy: 0.9609, test_Accuracy: 0.9615
Epoch: [ 0] [ 424/ 468] time: 27.4938, train_loss: 0.05896267, train_accuracy: 0.9844, test_Accuracy: 0.9622
Epoch: [ 0] [ 425/ 468] time: 27.5558, train_loss: 0.13265391, train_accuracy: 0.9688, test_Accuracy: 0.9603
Epoch: [ 0] [ 426/ 468] time: 27.6178, train_loss: 0.15410823, train_accuracy: 0.9531, test_Accuracy: 0.9589
Epoch: [ 0] [ 427/ 468] time: 27.6798, train_loss: 0.07289842, train_accuracy: 0.9922, test_Accuracy: 0.9582
Epoch: [ 0] [ 428/ 468] time: 27.7419, train_loss: 0.17787296, train_accuracy: 0.9453, test_Accuracy: 0.9574
Epoch: [ 0] [ 429/ 468] time: 27.8059, train_loss: 0.19533101, train_accuracy: 0.9609, test_Accuracy: 0.9583
Epoch: [ 0] [ 430/ 468] time: 27.9009, train_loss: 0.10289049, train_accuracy: 0.9766, test_Accuracy: 0.9593
Epoch: [ 0] [ 431/ 468] time: 28.0029, train_loss: 0.12447056, train_accuracy: 0.9531, test_Accuracy: 0.9610
Epoch: [ 0] [ 432/ 468] time: 28.0689, train_loss: 0.07770907, train_accuracy: 0.9922, test_Accuracy: 0.9610
Epoch: [ 0] [ 433/ 468] time: 28.1329, train_loss: 0.12110458, train_accuracy: 0.9688, test_Accuracy: 0.9603
Epoch: [ 0] [ 434/ 468] time: 28.1970, train_loss: 0.08781143, train_accuracy: 0.9688, test_Accuracy: 0.9589
Epoch: [ 0] [ 435/ 468] time: 28.2630, train_loss: 0.15456277, train_accuracy: 0.9453, test_Accuracy: 0.9577
Epoch: [ 0] [ 436/ 468] time: 28.3560, train_loss: 0.17653108, train_accuracy: 0.9609, test_Accuracy: 0.9562
Epoch: [ 0] [ 437/ 468] time: 28.4220, train_loss: 0.13572128, train_accuracy: 0.9844, test_Accuracy: 0.9560
Epoch: [ 0] [ 438/ 468] time: 28.4880, train_loss: 0.16228831, train_accuracy: 0.9531, test_Accuracy: 0.9569
Epoch: [ 0] [ 439/ 468] time: 28.5510, train_loss: 0.09951203, train_accuracy: 0.9609, test_Accuracy: 0.9576
Epoch: [ 0] [ 440/ 468] time: 28.6151, train_loss: 0.13474143, train_accuracy: 0.9531, test_Accuracy: 0.9577
Epoch: [ 0] [ 441/ 468] time: 28.6791, train_loss: 0.15225090, train_accuracy: 0.9453, test_Accuracy: 0.9589
Epoch: [ 0] [ 442/ 468] time: 28.7431, train_loss: 0.08897963, train_accuracy: 0.9688, test_Accuracy: 0.9592
Epoch: [ 0] [ 443/ 468] time: 28.8391, train_loss: 0.12807919, train_accuracy: 0.9609, test_Accuracy: 0.9594
Epoch: [ 0] [ 444/ 468] time: 28.9041, train_loss: 0.16098553, train_accuracy: 0.9531, test_Accuracy: 0.9590
Epoch: [ 0] [ 445/ 468] time: 28.9671, train_loss: 0.16510235, train_accuracy: 0.9688, test_Accuracy: 0.9590
Epoch: [ 0] [ 446/ 468] time: 29.0311, train_loss: 0.08558747, train_accuracy: 0.9688, test_Accuracy: 0.9593
Epoch: [ 0] [ 447/ 468] time: 29.0962, train_loss: 0.26763219, train_accuracy: 0.9375, test_Accuracy: 0.9597
Epoch: [ 0] [ 448/ 468] time: 29.1612, train_loss: 0.11790995, train_accuracy: 0.9531, test_Accuracy: 0.9610
Epoch: [ 0] [ 449/ 468] time: 29.2252, train_loss: 0.15260196, train_accuracy: 0.9453, test_Accuracy: 0.9616
Epoch: [ 0] [ 450/ 468] time: 29.2912, train_loss: 0.13379526, train_accuracy: 0.9609, test_Accuracy: 0.9626
Epoch: [ 0] [ 451/ 468] time: 29.3572, train_loss: 0.12205721, train_accuracy: 0.9609, test_Accuracy: 0.9617
Epoch: [ 0] [ 452/ 468] time: 29.4212, train_loss: 0.15094128, train_accuracy: 0.9609, test_Accuracy: 0.9617
Epoch: [ 0] [ 453/ 468] time: 29.4842, train_loss: 0.05792763, train_accuracy: 1.0000, test_Accuracy: 0.9605
Epoch: [ 0] [ 454/ 468] time: 29.5473, train_loss: 0.11666223, train_accuracy: 0.9688, test_Accuracy: 0.9603
Epoch: [ 0] [ 455/ 468] time: 29.6093, train_loss: 0.05687680, train_accuracy: 0.9844, test_Accuracy: 0.9594
Epoch: [ 0] [ 456/ 468] time: 29.6713, train_loss: 0.11365558, train_accuracy: 0.9609, test_Accuracy: 0.9581
Epoch: [ 0] [ 457/ 468] time: 29.7343, train_loss: 0.08995635, train_accuracy: 0.9688, test_Accuracy: 0.9578
Epoch: [ 0] [ 458/ 468] time: 29.8013, train_loss: 0.15706646, train_accuracy: 0.9609, test_Accuracy: 0.9594
Epoch: [ 0] [ 459/ 468] time: 29.9063, train_loss: 0.15029000, train_accuracy: 0.9531, test_Accuracy: 0.9622
Epoch: [ 0] [ 460/ 468] time: 29.9734, train_loss: 0.15182897, train_accuracy: 0.9531, test_Accuracy: 0.9638
Epoch: [ 0] [ 461/ 468] time: 30.0384, train_loss: 0.09535036, train_accuracy: 0.9688, test_Accuracy: 0.9635
Epoch: [ 0] [ 462/ 468] time: 30.1014, train_loss: 0.14583090, train_accuracy: 0.9531, test_Accuracy: 0.9621
Epoch: [ 0] [ 463/ 468] time: 30.1644, train_loss: 0.09659317, train_accuracy: 0.9844, test_Accuracy: 0.9600
Epoch: [ 0] [ 464/ 468] time: 30.2294, train_loss: 0.07517572, train_accuracy: 0.9766, test_Accuracy: 0.9593
Epoch: [ 0] [ 465/ 468] time: 30.2944, train_loss: 0.10643730, train_accuracy: 0.9766, test_Accuracy: 0.9589
Epoch: [ 0] [ 466/ 468] time: 30.3604, train_loss: 0.11571550, train_accuracy: 0.9688, test_Accuracy: 0.9598
Epoch: [ 0] [ 467/ 468] time: 30.4285, train_loss: 0.12712526, train_accuracy: 0.9688, test_Accuracy: 0.9607
Epoch: [ 0] [ 468/ 468] time: 30.4955, train_loss: 0.09152033, train_accuracy: 0.9688, test_Accuracy: 0.9618