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Commit c3426f6e authored by Sugon_ldc's avatar Sugon_ldc
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add shufflenetv2 model

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results/4090/230628003749.log 0 → 100644
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2023-06-28 00:37:49.978036 cuda
2023-06-28 00:38:04.069070 Train: epoch: 1 batch: 0/4, loss: 0.693067
2023-06-28 00:38:48.656869 Validation: avg loss: 0.6878, avg acc: 58.0952%
2023-06-28 00:39:02.850143 Train: epoch: 2 batch: 0/4, loss: 0.682373
2023-06-28 00:39:47.570461 Validation: avg loss: 0.6836, avg acc: 58.0952%
2023-06-28 00:40:01.779678 Train: epoch: 3 batch: 0/4, loss: 0.669462
2023-06-28 00:40:46.890763 Validation: avg loss: 0.6835, avg acc: 58.0952%
2023-06-28 00:41:01.120619 Train: epoch: 4 batch: 0/4, loss: 0.668123
2023-06-28 00:41:46.105633 Validation: avg loss: 0.6800, avg acc: 58.0952%
2023-06-28 00:42:00.231917 Train: epoch: 5 batch: 0/4, loss: 0.658780
2023-06-28 00:42:44.757819 Validation: avg loss: 0.6816, avg acc: 58.0952%
2023-06-28 00:42:59.100509 Train: epoch: 6 batch: 0/4, loss: 0.604294
2023-06-28 00:43:43.979090 Validation: avg loss: 0.6818, avg acc: 58.0952%
2023-06-28 00:43:58.482084 Train: epoch: 7 batch: 0/4, loss: 0.574335
2023-06-28 00:44:43.802849 Validation: avg loss: 0.6815, avg acc: 58.0952%
2023-06-28 00:44:58.409313 Train: epoch: 8 batch: 0/4, loss: 0.520112
2023-06-28 00:45:43.843513 Validation: avg loss: 0.6837, avg acc: 58.0952%
2023-06-28 00:45:58.441874 Train: epoch: 9 batch: 0/4, loss: 0.445718
2023-06-28 00:46:43.734008 Validation: avg loss: 0.6768, avg acc: 58.0952%
2023-06-28 00:46:58.006825 Train: epoch: 10 batch: 0/4, loss: 0.363334
2023-06-28 00:47:43.171838 Validation: avg loss: 0.6751, avg acc: 59.0476%
2023-06-28 00:47:57.698577 Train: epoch: 11 batch: 0/4, loss: 0.271284
2023-06-28 00:48:42.711409 Validation: avg loss: 0.6619, avg acc: 60.0000%
2023-06-28 00:48:56.879045 Train: epoch: 12 batch: 0/4, loss: 0.178573
2023-06-28 00:49:42.024172 Validation: avg loss: 0.6768, avg acc: 55.2381%
2023-06-28 00:49:56.148081 Train: epoch: 13 batch: 0/4, loss: 0.140371
2023-06-28 00:50:41.341682 Validation: avg loss: 0.7109, avg acc: 54.2857%
2023-06-28 00:50:55.610759 Train: epoch: 14 batch: 0/4, loss: 0.076913
2023-06-28 00:51:40.357213 Validation: avg loss: 0.8131, avg acc: 52.3810%
2023-06-28 00:51:54.583653 Train: epoch: 15 batch: 0/4, loss: 0.067480
2023-06-28 00:52:40.140130 Validation: avg loss: 0.9456, avg acc: 51.4286%
2023-06-28 00:52:54.397041 Train: epoch: 16 batch: 0/4, loss: 0.036423
2023-06-28 00:53:39.758018 Validation: avg loss: 1.0530, avg acc: 54.2857%
2023-06-28 00:53:54.317059 Train: epoch: 17 batch: 0/4, loss: 0.028782
2023-06-28 00:54:39.533123 Validation: avg loss: 1.1482, avg acc: 56.1905%
2023-06-28 00:54:53.869185 Train: epoch: 18 batch: 0/4, loss: 0.033167
2023-06-28 00:55:39.080207 Validation: avg loss: 1.1797, avg acc: 56.1905%
2023-06-28 00:55:53.126182 Train: epoch: 19 batch: 0/4, loss: 0.016202
2023-06-28 00:56:37.472863 Validation: avg loss: 1.2256, avg acc: 56.1905%
2023-06-28 00:56:51.590106 Train: epoch: 20 batch: 0/4, loss: 0.009053
2023-06-28 00:57:36.235819 Validation: avg loss: 1.2484, avg acc: 59.0476%
2023-06-28 00:57:50.430380 Train: epoch: 21 batch: 0/4, loss: 0.010292
2023-06-28 00:58:35.398258 Validation: avg loss: 1.3279, avg acc: 60.0000%
2023-06-28 00:58:49.667245 Train: epoch: 22 batch: 0/4, loss: 0.007880
2023-06-28 00:59:34.743015 Validation: avg loss: 1.3961, avg acc: 60.0000%
2023-06-28 00:59:48.869422 Train: epoch: 23 batch: 0/4, loss: 0.009219
2023-06-28 01:00:33.718706 Validation: avg loss: 1.4046, avg acc: 56.1905%
2023-06-28 01:00:47.940706 Train: epoch: 24 batch: 0/4, loss: 0.004677
2023-06-28 01:01:33.176631 Validation: avg loss: 1.3907, avg acc: 55.2381%
2023-06-28 01:01:47.409909 Train: epoch: 25 batch: 0/4, loss: 0.008842
2023-06-28 01:02:32.479701 Validation: avg loss: 1.4078, avg acc: 58.0952%
2023-06-28 01:02:46.858229 Train: epoch: 26 batch: 0/4, loss: 0.013503
2023-06-28 01:03:32.435670 Validation: avg loss: 1.4572, avg acc: 59.0476%
2023-06-28 01:03:47.368655 Train: epoch: 27 batch: 0/4, loss: 0.006626
2023-06-28 01:04:33.722813 Validation: avg loss: 1.4598, avg acc: 59.0476%
2023-06-28 01:04:48.451678 Train: epoch: 28 batch: 0/4, loss: 0.005886
2023-06-28 01:05:34.180051 Validation: avg loss: 1.4664, avg acc: 58.0952%
2023-06-28 01:05:48.434196 Train: epoch: 29 batch: 0/4, loss: 0.004940
2023-06-28 01:06:34.316611 Validation: avg loss: 1.4973, avg acc: 56.1905%
2023-06-28 01:06:48.578069 Train: epoch: 30 batch: 0/4, loss: 0.002945
2023-06-28 01:07:33.676803 Validation: avg loss: 1.5715, avg acc: 57.1429%
2023-06-28 01:07:48.010228 Train: epoch: 31 batch: 0/4, loss: 0.004101
2023-06-28 01:08:34.145123 Validation: avg loss: 1.5615, avg acc: 56.1905%
2023-06-28 01:08:48.956649 Train: epoch: 32 batch: 0/4, loss: 0.004165
2023-06-28 01:09:33.975731 Validation: avg loss: 1.5249, avg acc: 57.1429%
2023-06-28 01:09:48.523571 Train: epoch: 33 batch: 0/4, loss: 0.002205
2023-06-28 01:10:34.181073 Validation: avg loss: 1.5044, avg acc: 57.1429%
2023-06-28 01:10:48.667479 Train: epoch: 34 batch: 0/4, loss: 0.003043
2023-06-28 01:11:34.270933 Validation: avg loss: 1.5105, avg acc: 60.0000%
2023-06-28 01:11:48.475934 Train: epoch: 35 batch: 0/4, loss: 0.005146
2023-06-28 01:12:33.493880 Validation: avg loss: 1.4945, avg acc: 59.0476%
2023-06-28 01:12:47.731703 Train: epoch: 36 batch: 0/4, loss: 0.001391
2023-06-28 01:13:34.069553 Validation: avg loss: 1.4949, avg acc: 61.9048%
2023-06-28 01:13:51.020585 Train: epoch: 37 batch: 0/4, loss: 0.002418
2023-06-28 01:14:36.726945 Validation: avg loss: 1.4473, avg acc: 61.9048%
2023-06-28 01:14:50.933113 Train: epoch: 38 batch: 0/4, loss: 0.001485
2023-06-28 01:15:35.503621 Validation: avg loss: 1.4357, avg acc: 62.8571%
2023-06-28 01:15:49.666920 Train: epoch: 39 batch: 0/4, loss: 0.001815
2023-06-28 01:16:34.379353 Validation: avg loss: 1.4482, avg acc: 61.9048%
2023-06-28 01:16:48.543897 Train: epoch: 40 batch: 0/4, loss: 0.004567
2023-06-28 01:17:33.737475 Validation: avg loss: 1.4762, avg acc: 60.9524%
2023-06-28 01:17:47.935196 Train: epoch: 41 batch: 0/4, loss: 0.000961
2023-06-28 01:18:32.496347 Validation: avg loss: 1.4937, avg acc: 60.0000%
2023-06-28 01:18:46.739428 Train: epoch: 42 batch: 0/4, loss: 0.001626
2023-06-28 01:19:31.817522 Validation: avg loss: 1.5100, avg acc: 60.0000%
2023-06-28 01:19:46.275329 Train: epoch: 43 batch: 0/4, loss: 0.000995
2023-06-28 01:20:31.148739 Validation: avg loss: 1.5247, avg acc: 59.0476%
2023-06-28 01:20:45.390128 Train: epoch: 44 batch: 0/4, loss: 0.000796
2023-06-28 01:21:30.408919 Validation: avg loss: 1.5365, avg acc: 60.9524%
2023-06-28 01:21:44.821678 Train: epoch: 45 batch: 0/4, loss: 0.001251
2023-06-28 01:22:29.741533 Validation: avg loss: 1.5421, avg acc: 60.9524%
2023-06-28 01:22:44.346410 Train: epoch: 46 batch: 0/4, loss: 0.000667
2023-06-28 01:23:30.143946 Validation: avg loss: 1.5387, avg acc: 60.9524%
2023-06-28 01:23:44.816721 Train: epoch: 47 batch: 0/4, loss: 0.000684
2023-06-28 01:24:30.374873 Validation: avg loss: 1.5321, avg acc: 61.9048%
2023-06-28 01:24:44.932431 Train: epoch: 48 batch: 0/4, loss: 0.000721
2023-06-28 01:25:30.583908 Validation: avg loss: 1.5259, avg acc: 60.0000%
2023-06-28 01:25:44.973742 Train: epoch: 49 batch: 0/4, loss: 0.001524
2023-06-28 01:26:30.076239 Validation: avg loss: 1.5328, avg acc: 60.9524%
2023-06-28 01:26:44.301327 Train: epoch: 50 batch: 0/4, loss: 0.000452
2023-06-28 01:27:29.465129 Validation: avg loss: 1.5341, avg acc: 60.0000%
results/4090/230628012729.log 0 → 100644
View file @ c3426f6e
2023-06-28 01:27:29.498289 cuda
2023-06-28 01:27:44.178977 Train: epoch: 1 batch: 0/4, loss: 0.694665
2023-06-28 01:28:29.371643 Validation: avg loss: 0.6901, avg acc: 57.1429%
2023-06-28 01:28:43.662205 Train: epoch: 2 batch: 0/4, loss: 0.682679
2023-06-28 01:29:28.565725 Validation: avg loss: 0.6858, avg acc: 57.1429%
2023-06-28 01:29:43.024816 Train: epoch: 3 batch: 0/4, loss: 0.678544
2023-06-28 01:30:27.987655 Validation: avg loss: 0.6832, avg acc: 57.1429%
2023-06-28 01:30:42.736962 Train: epoch: 4 batch: 0/4, loss: 0.660189
2023-06-28 01:31:27.914631 Validation: avg loss: 0.6962, avg acc: 42.8571%
2023-06-28 01:31:42.189873 Train: epoch: 5 batch: 0/4, loss: 0.659297
2023-06-28 01:32:27.646864 Validation: avg loss: 0.6919, avg acc: 57.1429%
2023-06-28 01:32:41.879671 Train: epoch: 6 batch: 0/4, loss: 0.667431
2023-06-28 01:33:26.964715 Validation: avg loss: 0.7075, avg acc: 42.8571%
2023-06-28 01:33:41.063045 Train: epoch: 7 batch: 0/4, loss: 0.617283
2023-06-28 01:34:25.824487 Validation: avg loss: 0.6911, avg acc: 60.9524%
2023-06-28 01:34:40.323201 Train: epoch: 8 batch: 0/4, loss: 0.570127
2023-06-28 01:35:25.522091 Validation: avg loss: 0.6935, avg acc: 45.7143%
2023-06-28 01:35:39.993172 Train: epoch: 9 batch: 0/4, loss: 0.548969
2023-06-28 01:36:25.697874 Validation: avg loss: 0.7317, avg acc: 42.8571%
2023-06-28 01:36:39.958004 Train: epoch: 10 batch: 0/4, loss: 0.484664
2023-06-28 01:37:25.704423 Validation: avg loss: 0.7028, avg acc: 49.5238%
2023-06-28 01:37:40.575212 Train: epoch: 11 batch: 0/4, loss: 0.441138
2023-06-28 01:38:26.980730 Validation: avg loss: 0.7058, avg acc: 49.5238%
2023-06-28 01:38:41.458257 Train: epoch: 12 batch: 0/4, loss: 0.367233
2023-06-28 01:39:26.975605 Validation: avg loss: 0.7120, avg acc: 50.4762%
2023-06-28 01:39:41.237388 Train: epoch: 13 batch: 0/4, loss: 0.213344
2023-06-28 01:40:26.060785 Validation: avg loss: 0.6638, avg acc: 63.8095%
2023-06-28 01:40:40.391417 Train: epoch: 14 batch: 0/4, loss: 0.255976
2023-06-28 01:41:25.504246 Validation: avg loss: 0.6382, avg acc: 61.9048%
2023-06-28 01:41:39.780481 Train: epoch: 15 batch: 0/4, loss: 0.102169
2023-06-28 01:42:25.074077 Validation: avg loss: 0.7021, avg acc: 62.8571%
2023-06-28 01:42:39.402949 Train: epoch: 16 batch: 0/4, loss: 0.099181
2023-06-28 01:43:24.189954 Validation: avg loss: 0.7925, avg acc: 61.9048%
2023-06-28 01:43:38.854012 Train: epoch: 17 batch: 0/4, loss: 0.057370
2023-06-28 01:44:23.593833 Validation: avg loss: 0.9190, avg acc: 60.0000%
2023-06-28 01:44:38.082592 Train: epoch: 18 batch: 0/4, loss: 0.051161
2023-06-28 01:45:22.948901 Validation: avg loss: 1.1094, avg acc: 55.2381%
2023-06-28 01:45:37.120141 Train: epoch: 19 batch: 0/4, loss: 0.028416
2023-06-28 01:46:22.201313 Validation: avg loss: 1.1909, avg acc: 58.0952%
2023-06-28 01:46:36.615099 Train: epoch: 20 batch: 0/4, loss: 0.018945
2023-06-28 01:47:21.802085 Validation: avg loss: 1.2461, avg acc: 60.9524%
2023-06-28 01:47:36.094373 Train: epoch: 21 batch: 0/4, loss: 0.011535
2023-06-28 01:48:21.098979 Validation: avg loss: 1.2550, avg acc: 61.9048%
2023-06-28 01:48:35.396349 Train: epoch: 22 batch: 0/4, loss: 0.017535
2023-06-28 01:49:20.045914 Validation: avg loss: 1.1881, avg acc: 61.9048%
2023-06-28 01:49:34.214638 Train: epoch: 23 batch: 0/4, loss: 0.020899
2023-06-28 01:50:19.639548 Validation: avg loss: 1.2605, avg acc: 62.8571%
2023-06-28 01:50:33.690343 Train: epoch: 24 batch: 0/4, loss: 0.013029
2023-06-28 01:51:18.948007 Validation: avg loss: 1.4516, avg acc: 59.0476%
2023-06-28 01:51:33.346062 Train: epoch: 25 batch: 0/4, loss: 0.026440
2023-06-28 01:52:17.898719 Validation: avg loss: 1.4270, avg acc: 61.9048%
2023-06-28 01:52:32.075626 Train: epoch: 26 batch: 0/4, loss: 0.015461
2023-06-28 01:53:17.104184 Validation: avg loss: 1.4306, avg acc: 64.7619%
2023-06-28 01:53:31.273740 Train: epoch: 27 batch: 0/4, loss: 0.013917
2023-06-28 01:54:16.904646 Validation: avg loss: 1.4380, avg acc: 63.8095%
2023-06-28 01:54:31.165321 Train: epoch: 28 batch: 0/4, loss: 0.009092
2023-06-28 01:55:16.958980 Validation: avg loss: 1.4684, avg acc: 60.0000%
2023-06-28 01:55:31.080000 Train: epoch: 29 batch: 0/4, loss: 0.027234
2023-06-28 01:56:15.931152 Validation: avg loss: 1.5698, avg acc: 63.8095%
2023-06-28 01:56:30.209265 Train: epoch: 30 batch: 0/4, loss: 0.005990
2023-06-28 01:57:15.520408 Validation: avg loss: 1.5776, avg acc: 71.4286%
2023-06-28 01:57:29.918785 Train: epoch: 31 batch: 0/4, loss: 0.007899
2023-06-28 01:58:15.301725 Validation: avg loss: 1.6919, avg acc: 66.6667%
2023-06-28 01:58:30.041185 Train: epoch: 32 batch: 0/4, loss: 0.010279
2023-06-28 01:59:15.084527 Validation: avg loss: 1.8546, avg acc: 62.8571%
2023-06-28 01:59:29.231390 Train: epoch: 33 batch: 0/4, loss: 0.007202
2023-06-28 02:00:14.169353 Validation: avg loss: 1.7220, avg acc: 63.8095%
2023-06-28 02:00:28.461046 Train: epoch: 34 batch: 0/4, loss: 0.009014
2023-06-28 02:01:13.840142 Validation: avg loss: 1.5476, avg acc: 66.6667%
2023-06-28 02:01:28.402509 Train: epoch: 35 batch: 0/4, loss: 0.011448
2023-06-28 02:02:14.837432 Validation: avg loss: 1.4587, avg acc: 64.7619%
2023-06-28 02:02:29.477797 Train: epoch: 36 batch: 0/4, loss: 0.004358
2023-06-28 02:03:14.868202 Validation: avg loss: 1.5383, avg acc: 60.0000%
2023-06-28 02:03:29.201787 Train: epoch: 37 batch: 0/4, loss: 0.009107
2023-06-28 02:04:13.978770 Validation: avg loss: 1.6280, avg acc: 60.0000%
2023-06-28 02:04:28.043453 Train: epoch: 38 batch: 0/4, loss: 0.012865
2023-06-28 02:05:13.314995 Validation: avg loss: 1.4671, avg acc: 60.9524%
2023-06-28 02:05:27.596150 Train: epoch: 39 batch: 0/4, loss: 0.009916
2023-06-28 02:06:14.256498 Validation: avg loss: 1.5654, avg acc: 67.6190%
2023-06-28 02:06:31.292249 Train: epoch: 40 batch: 0/4, loss: 0.004399
2023-06-28 02:07:25.473131 Validation: avg loss: 1.6744, avg acc: 67.6190%
2023-06-28 02:07:42.836883 Train: epoch: 41 batch: 0/4, loss: 0.005982
2023-06-28 02:08:37.663422 Validation: avg loss: 1.6835, avg acc: 64.7619%
2023-06-28 02:08:54.637783 Train: epoch: 42 batch: 0/4, loss: 0.003240
2023-06-28 02:09:47.997422 Validation: avg loss: 1.6657, avg acc: 63.8095%
2023-06-28 02:10:04.771465 Train: epoch: 43 batch: 0/4, loss: 0.006902
2023-06-28 02:10:58.377040 Validation: avg loss: 1.7045, avg acc: 65.7143%
2023-06-28 02:11:15.395956 Train: epoch: 44 batch: 0/4, loss: 0.010419
2023-06-28 02:12:09.470289 Validation: avg loss: 1.7507, avg acc: 66.6667%
2023-06-28 02:12:26.317963 Train: epoch: 45 batch: 0/4, loss: 0.003494
2023-06-28 02:13:14.431945 Validation: avg loss: 1.7484, avg acc: 66.6667%
2023-06-28 02:13:28.901484 Train: epoch: 46 batch: 0/4, loss: 0.001555
2023-06-28 02:14:13.942030 Validation: avg loss: 1.8422, avg acc: 60.9524%
2023-06-28 02:14:28.333940 Train: epoch: 47 batch: 0/4, loss: 0.003026
2023-06-28 02:15:13.232646 Validation: avg loss: 2.1813, avg acc: 60.9524%
2023-06-28 02:15:27.601842 Train: epoch: 48 batch: 0/4, loss: 0.001304
2023-06-28 02:16:12.214901 Validation: avg loss: 2.1655, avg acc: 61.9048%
2023-06-28 02:16:26.543083 Train: epoch: 49 batch: 0/4, loss: 0.003734
2023-06-28 02:17:11.206616 Validation: avg loss: 2.1269, avg acc: 60.0000%
2023-06-28 02:17:25.427501 Train: epoch: 50 batch: 0/4, loss: 0.008758
2023-06-28 02:18:10.157643 Validation: avg loss: 2.0094, avg acc: 62.8571%
results/4090/230628021810.log 0 → 100644
View file @ c3426f6e
2023-06-28 02:18:10.191347 cuda
2023-06-28 02:18:24.590105 Train: epoch: 1 batch: 0/4, loss: 0.690249
2023-06-28 02:19:09.969172 Validation: avg loss: 0.6905, avg acc: 58.0952%
2023-06-28 02:19:24.498012 Train: epoch: 2 batch: 0/4, loss: 0.682551
2023-06-28 02:20:12.924872 Validation: avg loss: 0.6843, avg acc: 58.0952%
2023-06-28 02:20:27.411335 Train: epoch: 3 batch: 0/4, loss: 0.691065
2023-06-28 02:21:12.226704 Validation: avg loss: 0.6844, avg acc: 58.0952%
2023-06-28 02:21:26.494891 Train: epoch: 4 batch: 0/4, loss: 0.656486
2023-06-28 02:22:11.201627 Validation: avg loss: 0.6885, avg acc: 58.0952%
2023-06-28 02:22:25.324919 Train: epoch: 5 batch: 0/4, loss: 0.641778
2023-06-28 02:23:10.589578 Validation: avg loss: 0.6955, avg acc: 41.9048%
2023-06-28 02:23:24.986591 Train: epoch: 6 batch: 0/4, loss: 0.635714
2023-06-28 02:24:09.995102 Validation: avg loss: 0.6892, avg acc: 58.0952%
2023-06-28 02:24:24.299061 Train: epoch: 7 batch: 0/4, loss: 0.588812
2023-06-28 02:25:09.383180 Validation: avg loss: 0.7100, avg acc: 41.9048%
2023-06-28 02:25:23.459523 Train: epoch: 8 batch: 0/4, loss: 0.580687
2023-06-28 02:26:08.476721 Validation: avg loss: 0.6989, avg acc: 43.8095%
2023-06-28 02:26:22.636932 Train: epoch: 9 batch: 0/4, loss: 0.538401
2023-06-28 02:27:07.755846 Validation: avg loss: 0.7032, avg acc: 44.7619%
2023-06-28 02:27:22.308433 Train: epoch: 10 batch: 0/4, loss: 0.479063
2023-06-28 02:28:08.023222 Validation: avg loss: 0.7259, avg acc: 43.8095%
2023-06-28 02:28:22.212473 Train: epoch: 11 batch: 0/4, loss: 0.399180
2023-06-28 02:29:07.187142 Validation: avg loss: 0.6419, avg acc: 66.6667%
2023-06-28 02:29:21.459886 Train: epoch: 12 batch: 0/4, loss: 0.374041
2023-06-28 02:30:06.783311 Validation: avg loss: 0.6404, avg acc: 63.8095%
2023-06-28 02:30:20.925891 Train: epoch: 13 batch: 0/4, loss: 0.199607
2023-06-28 02:31:09.303999 Validation: avg loss: 0.6425, avg acc: 64.7619%
2023-06-28 02:31:26.317069 Train: epoch: 14 batch: 0/4, loss: 0.210665
2023-06-28 02:32:14.248001 Validation: avg loss: 0.6495, avg acc: 64.7619%
2023-06-28 02:32:28.770466 Train: epoch: 15 batch: 0/4, loss: 0.094463
2023-06-28 02:33:13.835456 Validation: avg loss: 0.6781, avg acc: 64.7619%
2023-06-28 02:33:28.075316 Train: epoch: 16 batch: 0/4, loss: 0.071825
2023-06-28 02:34:13.056136 Validation: avg loss: 0.6933, avg acc: 66.6667%
2023-06-28 02:34:27.574360 Train: epoch: 17 batch: 0/4, loss: 0.075157
2023-06-28 02:35:12.945181 Validation: avg loss: 0.7505, avg acc: 64.7619%
2023-06-28 02:35:27.190267 Train: epoch: 18 batch: 0/4, loss: 0.035870
2023-06-28 02:36:12.281194 Validation: avg loss: 0.8531, avg acc: 64.7619%
2023-06-28 02:36:26.858452 Train: epoch: 19 batch: 0/4, loss: 0.024990
2023-06-28 02:37:12.890268 Validation: avg loss: 0.9023, avg acc: 64.7619%
2023-06-28 02:37:27.425535 Train: epoch: 20 batch: 0/4, loss: 0.019082
2023-06-28 02:38:12.722207 Validation: avg loss: 0.9582, avg acc: 66.6667%
2023-06-28 02:38:27.265360 Train: epoch: 21 batch: 0/4, loss: 0.010728
2023-06-28 02:39:12.364647 Validation: avg loss: 0.9925, avg acc: 65.7143%
2023-06-28 02:39:26.689739 Train: epoch: 22 batch: 0/4, loss: 0.023405
2023-06-28 02:40:11.877307 Validation: avg loss: 1.0391, avg acc: 63.8095%
2023-06-28 02:40:26.261142 Train: epoch: 23 batch: 0/4, loss: 0.006633
2023-06-28 02:41:11.681557 Validation: avg loss: 1.0852, avg acc: 64.7619%
2023-06-28 02:41:25.847109 Train: epoch: 24 batch: 0/4, loss: 0.015039
2023-06-28 02:42:10.949129 Validation: avg loss: 1.2231, avg acc: 66.6667%
2023-06-28 02:42:25.108043 Train: epoch: 25 batch: 0/4, loss: 0.017307
2023-06-28 02:43:10.040710 Validation: avg loss: 1.1383, avg acc: 62.8571%
2023-06-28 02:43:24.367214 Train: epoch: 26 batch: 0/4, loss: 0.009442
2023-06-28 02:44:10.201937 Validation: avg loss: 1.1565, avg acc: 64.7619%
2023-06-28 02:44:24.655029 Train: epoch: 27 batch: 0/4, loss: 0.008642
2023-06-28 02:45:16.466766 Validation: avg loss: 1.2177, avg acc: 65.7143%
2023-06-28 02:45:34.059083 Train: epoch: 28 batch: 0/4, loss: 0.004727
2023-06-28 02:46:26.336540 Validation: avg loss: 1.2653, avg acc: 63.8095%
2023-06-28 02:46:43.131403 Train: epoch: 29 batch: 0/4, loss: 0.003736
2023-06-28 02:47:36.324387 Validation: avg loss: 1.3018, avg acc: 64.7619%
2023-06-28 02:47:53.401691 Train: epoch: 30 batch: 0/4, loss: 0.004438
2023-06-28 02:48:48.112933 Validation: avg loss: 1.3224, avg acc: 63.8095%
2023-06-28 02:49:05.076562 Train: epoch: 31 batch: 0/4, loss: 0.004074
2023-06-28 02:49:58.477870 Validation: avg loss: 1.3503, avg acc: 62.8571%
2023-06-28 02:50:15.549796 Train: epoch: 32 batch: 0/4, loss: 0.005016
2023-06-28 02:51:08.879135 Validation: avg loss: 1.3151, avg acc: 61.9048%
2023-06-28 02:51:25.677918 Train: epoch: 33 batch: 0/4, loss: 0.002044
2023-06-28 02:52:20.105423 Validation: avg loss: 1.2926, avg acc: 62.8571%
2023-06-28 02:52:37.041849 Train: epoch: 34 batch: 0/4, loss: 0.002703
2023-06-28 02:53:31.706319 Validation: avg loss: 1.2878, avg acc: 63.8095%
2023-06-28 02:53:49.176129 Train: epoch: 35 batch: 0/4, loss: 0.003849
2023-06-28 02:54:42.142647 Validation: avg loss: 1.3129, avg acc: 63.8095%
2023-06-28 02:54:56.427546 Train: epoch: 36 batch: 0/4, loss: 0.001835
2023-06-28 02:55:41.494277 Validation: avg loss: 1.3500, avg acc: 61.9048%
2023-06-28 02:55:55.778091 Train: epoch: 37 batch: 0/4, loss: 0.002286
2023-06-28 02:56:41.046795 Validation: avg loss: 1.3715, avg acc: 62.8571%
2023-06-28 02:56:55.370527 Train: epoch: 38 batch: 0/4, loss: 0.001753
2023-06-28 02:57:40.945144 Validation: avg loss: 1.3863, avg acc: 64.7619%
2023-06-28 02:57:55.244577 Train: epoch: 39 batch: 0/4, loss: 0.001636
2023-06-28 02:58:40.557081 Validation: avg loss: 1.4102, avg acc: 64.7619%
2023-06-28 02:58:54.994770 Train: epoch: 40 batch: 0/4, loss: 0.001649
2023-06-28 02:59:39.954962 Validation: avg loss: 1.4169, avg acc: 63.8095%
2023-06-28 02:59:54.214338 Train: epoch: 41 batch: 0/4, loss: 0.001002
2023-06-28 03:00:39.437733 Validation: avg loss: 1.4182, avg acc: 62.8571%
2023-06-28 03:00:53.650708 Train: epoch: 42 batch: 0/4, loss: 0.001196
2023-06-28 03:01:39.005193 Validation: avg loss: 1.4063, avg acc: 62.8571%
2023-06-28 03:01:53.324523 Train: epoch: 43 batch: 0/4, loss: 0.000889
2023-06-28 03:02:38.746603 Validation: avg loss: 1.4078, avg acc: 63.8095%
2023-06-28 03:02:52.879053 Train: epoch: 44 batch: 0/4, loss: 0.001685
2023-06-28 03:03:37.789140 Validation: avg loss: 1.3901, avg acc: 63.8095%
2023-06-28 03:03:51.837770 Train: epoch: 45 batch: 0/4, loss: 0.001467
2023-06-28 03:04:37.321315 Validation: avg loss: 1.4143, avg acc: 63.8095%
2023-06-28 03:04:51.924517 Train: epoch: 46 batch: 0/4, loss: 0.001200
2023-06-28 03:05:37.733740 Validation: avg loss: 1.4240, avg acc: 63.8095%
2023-06-28 03:05:51.972393 Train: epoch: 47 batch: 0/4, loss: 0.000806
2023-06-28 03:06:39.101561 Validation: avg loss: 1.4204, avg acc: 63.8095%
2023-06-28 03:06:53.373502 Train: epoch: 48 batch: 0/4, loss: 0.000644
2023-06-28 03:07:38.640922 Validation: avg loss: 1.4185, avg acc: 62.8571%
2023-06-28 03:07:53.026508 Train: epoch: 49 batch: 0/4, loss: 0.000653
2023-06-28 03:08:38.046226 Validation: avg loss: 1.3947, avg acc: 62.8571%
2023-06-28 03:08:52.122996 Train: epoch: 50 batch: 0/4, loss: 0.000570
2023-06-28 03:09:37.119446 Validation: avg loss: 1.3931, avg acc: 62.8571%
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2023-06-28 03:09:37.154578 cuda
2023-06-28 03:09:51.449183 Train: epoch: 1 batch: 0/4, loss: 0.693002
2023-06-28 03:10:37.293540 Validation: avg loss: 0.6892, avg acc: 58.0952%
2023-06-28 03:10:51.688451 Train: epoch: 2 batch: 0/4, loss: 0.687561
2023-06-28 03:11:36.852454 Validation: avg loss: 0.6810, avg acc: 58.0952%
2023-06-28 03:11:50.960553 Train: epoch: 3 batch: 0/4, loss: 0.681805
2023-06-28 03:12:36.129192 Validation: avg loss: 0.6913, avg acc: 58.0952%
2023-06-28 03:12:50.476572 Train: epoch: 4 batch: 0/4, loss: 0.665961
2023-06-28 03:13:35.487521 Validation: avg loss: 0.6886, avg acc: 58.0952%
2023-06-28 03:13:49.861003 Train: epoch: 5 batch: 0/4, loss: 0.644194
2023-06-28 03:14:35.147609 Validation: avg loss: 0.6877, avg acc: 58.0952%
2023-06-28 03:14:49.535890 Train: epoch: 6 batch: 0/4, loss: 0.622670
2023-06-28 03:15:34.784671 Validation: avg loss: 0.6870, avg acc: 58.0952%
2023-06-28 03:15:49.337855 Train: epoch: 7 batch: 0/4, loss: 0.599810
2023-06-28 03:16:34.214166 Validation: avg loss: 0.6969, avg acc: 41.9048%
2023-06-28 03:16:48.536066 Train: epoch: 8 batch: 0/4, loss: 0.545521
2023-06-28 03:17:33.960131 Validation: avg loss: 0.6841, avg acc: 55.2381%
2023-06-28 03:17:48.281651 Train: epoch: 9 batch: 0/4, loss: 0.452313
2023-06-28 03:18:33.276105 Validation: avg loss: 0.6612, avg acc: 72.3810%
2023-06-28 03:18:47.585477 Train: epoch: 10 batch: 0/4, loss: 0.390662
2023-06-28 03:19:32.407151 Validation: avg loss: 0.6701, avg acc: 50.4762%
2023-06-28 03:19:46.747612 Train: epoch: 11 batch: 0/4, loss: 0.324581
2023-06-28 03:20:31.709394 Validation: avg loss: 0.5978, avg acc: 67.6190%
2023-06-28 03:20:45.958098 Train: epoch: 12 batch: 0/4, loss: 0.254065
2023-06-28 03:21:31.222952 Validation: avg loss: 0.5616, avg acc: 68.5714%
2023-06-28 03:21:45.962876 Train: epoch: 13 batch: 0/4, loss: 0.200924
2023-06-28 03:22:31.271403 Validation: avg loss: 0.5085, avg acc: 71.4286%
2023-06-28 03:22:45.628448 Train: epoch: 14 batch: 0/4, loss: 0.124987
2023-06-28 03:23:32.817547 Validation: avg loss: 0.4824, avg acc: 75.2381%
2023-06-28 03:23:47.162008 Train: epoch: 15 batch: 0/4, loss: 0.090232
2023-06-28 03:24:32.146921 Validation: avg loss: 0.5138, avg acc: 70.4762%
2023-06-28 03:24:46.642517 Train: epoch: 16 batch: 0/4, loss: 0.083759
2023-06-28 03:25:32.353083 Validation: avg loss: 0.5420, avg acc: 69.5238%
2023-06-28 03:25:47.131522 Train: epoch: 17 batch: 0/4, loss: 0.066651
2023-06-28 03:26:32.781357 Validation: avg loss: 0.5527, avg acc: 73.3333%
2023-06-28 03:26:47.379127 Train: epoch: 18 batch: 0/4, loss: 0.024152
2023-06-28 03:27:33.147812 Validation: avg loss: 0.5908, avg acc: 71.4286%
2023-06-28 03:27:47.568344 Train: epoch: 19 batch: 0/4, loss: 0.017142
2023-06-28 03:28:33.092234 Validation: avg loss: 0.5997, avg acc: 70.4762%
2023-06-28 03:28:47.603911 Train: epoch: 20 batch: 0/4, loss: 0.012679
2023-06-28 03:29:33.533011 Validation: avg loss: 0.6142, avg acc: 76.1905%
2023-06-28 03:29:48.120699 Train: epoch: 21 batch: 0/4, loss: 0.019406
2023-06-28 03:30:33.497260 Validation: avg loss: 0.6960, avg acc: 74.2857%
2023-06-28 03:30:47.796580 Train: epoch: 22 batch: 0/4, loss: 0.023462
2023-06-28 03:31:32.909514 Validation: avg loss: 0.7432, avg acc: 73.3333%
2023-06-28 03:31:47.165250 Train: epoch: 23 batch: 0/4, loss: 0.010763
2023-06-28 03:32:31.899234 Validation: avg loss: 0.7907, avg acc: 72.3810%
2023-06-28 03:32:46.438450 Train: epoch: 24 batch: 0/4, loss: 0.013663
2023-06-28 03:33:31.940938 Validation: avg loss: 0.7950, avg acc: 68.5714%
2023-06-28 03:33:46.516226 Train: epoch: 25 batch: 0/4, loss: 0.006725
2023-06-28 03:34:32.670997 Validation: avg loss: 0.8249, avg acc: 69.5238%
2023-06-28 03:34:47.337794 Train: epoch: 26 batch: 0/4, loss: 0.004846
2023-06-28 03:35:33.504480 Validation: avg loss: 0.8479, avg acc: 72.3810%
2023-06-28 03:35:47.772504 Train: epoch: 27 batch: 0/4, loss: 0.005277
2023-06-28 03:36:32.575498 Validation: avg loss: 0.8493, avg acc: 72.3810%
2023-06-28 03:36:46.665919 Train: epoch: 28 batch: 0/4, loss: 0.011446
2023-06-28 03:37:31.196006 Validation: avg loss: 0.8339, avg acc: 71.4286%
2023-06-28 03:37:45.377302 Train: epoch: 29 batch: 0/4, loss: 0.003922
2023-06-28 03:38:30.580648 Validation: avg loss: 0.8505, avg acc: 72.3810%
2023-06-28 03:38:44.775296 Train: epoch: 30 batch: 0/4, loss: 0.002529
2023-06-28 03:39:29.493308 Validation: avg loss: 0.8773, avg acc: 71.4286%
2023-06-28 03:39:43.608244 Train: epoch: 31 batch: 0/4, loss: 0.001875
2023-06-28 03:40:28.207967 Validation: avg loss: 0.8870, avg acc: 70.4762%
2023-06-28 03:40:42.352605 Train: epoch: 32 batch: 0/4, loss: 0.002372
2023-06-28 03:41:27.482733 Validation: avg loss: 0.8911, avg acc: 72.3810%
2023-06-28 03:41:41.670935 Train: epoch: 33 batch: 0/4, loss: 0.003411
2023-06-28 03:42:26.716992 Validation: avg loss: 0.8858, avg acc: 72.3810%
2023-06-28 03:42:41.008464 Train: epoch: 34 batch: 0/4, loss: 0.002562
2023-06-28 03:43:26.039612 Validation: avg loss: 0.8796, avg acc: 72.3810%
2023-06-28 03:43:40.260189 Train: epoch: 35 batch: 0/4, loss: 0.003069
2023-06-28 03:44:25.722423 Validation: avg loss: 0.8868, avg acc: 72.3810%
2023-06-28 03:44:39.923484 Train: epoch: 36 batch: 0/4, loss: 0.001231
2023-06-28 03:45:25.127050 Validation: avg loss: 0.8910, avg acc: 73.3333%
2023-06-28 03:45:39.527841 Train: epoch: 37 batch: 0/4, loss: 0.001668
2023-06-28 03:46:24.319152 Validation: avg loss: 0.8928, avg acc: 73.3333%
2023-06-28 03:46:38.520530 Train: epoch: 38 batch: 0/4, loss: 0.001577
2023-06-28 03:47:23.353992 Validation: avg loss: 0.8915, avg acc: 73.3333%
2023-06-28 03:47:37.434393 Train: epoch: 39 batch: 0/4, loss: 0.001705
2023-06-28 03:48:22.834245 Validation: avg loss: 0.8936, avg acc: 73.3333%
2023-06-28 03:48:37.254894 Train: epoch: 40 batch: 0/4, loss: 0.001388
2023-06-28 03:49:22.471289 Validation: avg loss: 0.9009, avg acc: 73.3333%
2023-06-28 03:49:36.564547 Train: epoch: 41 batch: 0/4, loss: 0.000861
2023-06-28 03:50:21.412126 Validation: avg loss: 0.9012, avg acc: 74.2857%
2023-06-28 03:50:35.489009 Train: epoch: 42 batch: 0/4, loss: 0.001102
2023-06-28 03:51:20.143782 Validation: avg loss: 0.9065, avg acc: 74.2857%
2023-06-28 03:51:34.559930 Train: epoch: 43 batch: 0/4, loss: 0.000969
2023-06-28 03:52:19.340878 Validation: avg loss: 0.9071, avg acc: 74.2857%
2023-06-28 03:52:33.401578 Train: epoch: 44 batch: 0/4, loss: 0.000865
2023-06-28 03:53:18.033301 Validation: avg loss: 0.9177, avg acc: 73.3333%
2023-06-28 03:53:32.150168 Train: epoch: 45 batch: 0/4, loss: 0.000941
2023-06-28 03:54:17.146896 Validation: avg loss: 0.9327, avg acc: 74.2857%
2023-06-28 03:54:31.352235 Train: epoch: 46 batch: 0/4, loss: 0.000957
2023-06-28 03:55:17.407628 Validation: avg loss: 0.9416, avg acc: 74.2857%
2023-06-28 03:55:31.626750 Train: epoch: 47 batch: 0/4, loss: 0.000746
2023-06-28 03:56:16.058637 Validation: avg loss: 0.9409, avg acc: 74.2857%
2023-06-28 03:56:30.100803 Train: epoch: 48 batch: 0/4, loss: 0.001412
2023-06-28 03:57:14.611239 Validation: avg loss: 0.9413, avg acc: 73.3333%
2023-06-28 03:57:28.762473 Train: epoch: 49 batch: 0/4, loss: 0.000834
2023-06-28 03:58:13.474519 Validation: avg loss: 0.9323, avg acc: 75.2381%
2023-06-28 03:58:27.820951 Train: epoch: 50 batch: 0/4, loss: 0.000935
2023-06-28 03:59:12.546035 Validation: avg loss: 0.9237, avg acc: 74.2857%
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2023-06-28 03:59:12.580120 cuda
2023-06-28 03:59:26.750708 Train: epoch: 1 batch: 0/4, loss: 0.694612
2023-06-28 04:00:11.426282 Validation: avg loss: 0.6861, avg acc: 59.0476%
2023-06-28 04:00:25.908546 Train: epoch: 2 batch: 0/4, loss: 0.706914
2023-06-28 04:01:11.184999 Validation: avg loss: 0.6830, avg acc: 59.0476%
2023-06-28 04:01:25.798044 Train: epoch: 3 batch: 0/4, loss: 0.680926
2023-06-28 04:02:12.099444 Validation: avg loss: 0.6799, avg acc: 59.0476%
2023-06-28 04:02:26.460111 Train: epoch: 4 batch: 0/4, loss: 0.667983
2023-06-28 04:03:10.998068 Validation: avg loss: 0.6784, avg acc: 59.0476%
2023-06-28 04:03:25.196184 Train: epoch: 5 batch: 0/4, loss: 0.658656
2023-06-28 04:04:09.841678 Validation: avg loss: 0.6800, avg acc: 59.0476%
2023-06-28 04:04:24.079110 Train: epoch: 6 batch: 0/4, loss: 0.663088
2023-06-28 04:05:08.763914 Validation: avg loss: 0.6773, avg acc: 59.0476%
2023-06-28 04:05:23.003873 Train: epoch: 7 batch: 0/4, loss: 0.629996
2023-06-28 04:06:08.637310 Validation: avg loss: 0.6743, avg acc: 59.0476%
2023-06-28 04:06:23.407611 Train: epoch: 8 batch: 0/4, loss: 0.597220
2023-06-28 04:07:08.242516 Validation: avg loss: 0.6800, avg acc: 59.0476%
2023-06-28 04:07:22.526979 Train: epoch: 9 batch: 0/4, loss: 0.537858
2023-06-28 04:08:07.101244 Validation: avg loss: 0.6709, avg acc: 59.0476%
2023-06-28 04:08:21.328130 Train: epoch: 10 batch: 0/4, loss: 0.478460
2023-06-28 04:09:05.828543 Validation: avg loss: 0.6716, avg acc: 63.8095%
2023-06-28 04:09:19.873732 Train: epoch: 11 batch: 0/4, loss: 0.395090
2023-06-28 04:10:05.058702 Validation: avg loss: 0.6379, avg acc: 65.7143%
2023-06-28 04:10:19.285747 Train: epoch: 12 batch: 0/4, loss: 0.331272
2023-06-28 04:11:03.953933 Validation: avg loss: 0.6334, avg acc: 61.9048%
2023-06-28 04:11:18.090531 Train: epoch: 13 batch: 0/4, loss: 0.270222
2023-06-28 04:12:02.755340 Validation: avg loss: 0.5858, avg acc: 70.4762%
2023-06-28 04:12:16.950131 Train: epoch: 14 batch: 0/4, loss: 0.168852
2023-06-28 04:13:01.884081 Validation: avg loss: 0.5932, avg acc: 64.7619%
2023-06-28 04:13:16.075769 Train: epoch: 15 batch: 0/4, loss: 0.140117
2023-06-28 04:14:01.014580 Validation: avg loss: 0.6184, avg acc: 67.6190%
2023-06-28 04:14:15.406445 Train: epoch: 16 batch: 0/4, loss: 0.100922
2023-06-28 04:15:00.611265 Validation: avg loss: 0.6841, avg acc: 62.8571%
2023-06-28 04:15:14.772177 Train: epoch: 17 batch: 0/4, loss: 0.064960
2023-06-28 04:15:59.270308 Validation: avg loss: 0.8381, avg acc: 60.0000%
2023-06-28 04:16:13.358347 Train: epoch: 18 batch: 0/4, loss: 0.041114
2023-06-28 04:16:58.412814 Validation: avg loss: 0.8127, avg acc: 63.8095%
2023-06-28 04:17:12.859787 Train: epoch: 19 batch: 0/4, loss: 0.028654
2023-06-28 04:17:58.746568 Validation: avg loss: 0.8722, avg acc: 63.8095%
2023-06-28 04:18:13.193857 Train: epoch: 20 batch: 0/4, loss: 0.020548
2023-06-28 04:18:58.557002 Validation: avg loss: 1.0138, avg acc: 63.8095%
2023-06-28 04:19:12.811166 Train: epoch: 21 batch: 0/4, loss: 0.024834
2023-06-28 04:19:57.383134 Validation: avg loss: 1.1134, avg acc: 65.7143%
2023-06-28 04:20:11.593541 Train: epoch: 22 batch: 0/4, loss: 0.024447
2023-06-28 04:20:56.144567 Validation: avg loss: 1.1052, avg acc: 61.9048%
2023-06-28 04:21:10.280428 Train: epoch: 23 batch: 0/4, loss: 0.024402
2023-06-28 04:21:55.164144 Validation: avg loss: 1.0037, avg acc: 62.8571%
2023-06-28 04:22:09.484836 Train: epoch: 24 batch: 0/4, loss: 0.026940
2023-06-28 04:22:54.337155 Validation: avg loss: 1.1395, avg acc: 65.7143%
2023-06-28 04:23:08.575422 Train: epoch: 25 batch: 0/4, loss: 0.020789
2023-06-28 04:23:53.433541 Validation: avg loss: 1.1802, avg acc: 65.7143%
2023-06-28 04:24:07.944800 Train: epoch: 26 batch: 0/4, loss: 0.017444
2023-06-28 04:24:53.299679 Validation: avg loss: 1.0212, avg acc: 67.6190%
2023-06-28 04:25:07.765353 Train: epoch: 27 batch: 0/4, loss: 0.013250
2023-06-28 04:25:52.812562 Validation: avg loss: 0.9592, avg acc: 68.5714%
2023-06-28 04:26:07.260835 Train: epoch: 28 batch: 0/4, loss: 0.011131
2023-06-28 04:26:52.346310 Validation: avg loss: 1.1193, avg acc: 72.3810%
2023-06-28 04:27:06.644760 Train: epoch: 29 batch: 0/4, loss: 0.026329
2023-06-28 04:27:51.863781 Validation: avg loss: 1.1687, avg acc: 64.7619%
2023-06-28 04:28:05.962933 Train: epoch: 30 batch: 0/4, loss: 0.011010
2023-06-28 04:28:50.606240 Validation: avg loss: 1.1444, avg acc: 63.8095%
2023-06-28 04:29:04.812994 Train: epoch: 31 batch: 0/4, loss: 0.007555
2023-06-28 04:29:49.894941 Validation: avg loss: 1.2691, avg acc: 69.5238%
2023-06-28 04:30:04.020104 Train: epoch: 32 batch: 0/4, loss: 0.026084
2023-06-28 04:30:48.602736 Validation: avg loss: 1.3224, avg acc: 65.7143%
2023-06-28 04:31:02.668182 Train: epoch: 33 batch: 0/4, loss: 0.016953
2023-06-28 04:31:47.391245 Validation: avg loss: 1.3359, avg acc: 63.8095%
2023-06-28 04:32:01.694358 Train: epoch: 34 batch: 0/4, loss: 0.007214
2023-06-28 04:32:46.917156 Validation: avg loss: 1.2960, avg acc: 64.7619%
2023-06-28 04:33:01.216805 Train: epoch: 35 batch: 0/4, loss: 0.016135
2023-06-28 04:33:52.554001 Validation: avg loss: 1.3414, avg acc: 66.6667%
2023-06-28 04:34:09.581260 Train: epoch: 36 batch: 0/4, loss: 0.005827
2023-06-28 04:35:03.567810 Validation: avg loss: 1.4429, avg acc: 63.8095%
2023-06-28 04:35:20.656888 Train: epoch: 37 batch: 0/4, loss: 0.003277
2023-06-28 04:36:14.468588 Validation: avg loss: 1.5331, avg acc: 63.8095%
2023-06-28 04:36:31.559136 Train: epoch: 38 batch: 0/4, loss: 0.006702
2023-06-28 04:37:25.303902 Validation: avg loss: 1.5363, avg acc: 64.7619%
2023-06-28 04:37:42.401555 Train: epoch: 39 batch: 0/4, loss: 0.006998
2023-06-28 04:38:35.539481 Validation: avg loss: 1.5584, avg acc: 65.7143%
2023-06-28 04:38:52.511838 Train: epoch: 40 batch: 0/4, loss: 0.004243
2023-06-28 04:39:40.018935 Validation: avg loss: 1.5435, avg acc: 64.7619%
2023-06-28 04:39:54.218691 Train: epoch: 41 batch: 0/4, loss: 0.005694
2023-06-28 04:40:39.406743 Validation: avg loss: 1.5224, avg acc: 65.7143%
2023-06-28 04:40:53.614415 Train: epoch: 42 batch: 0/4, loss: 0.002508
2023-06-28 04:41:38.977191 Validation: avg loss: 1.4839, avg acc: 62.8571%
2023-06-28 04:41:53.546677 Train: epoch: 43 batch: 0/4, loss: 0.005065
2023-06-28 04:42:38.615712 Validation: avg loss: 1.4694, avg acc: 62.8571%
2023-06-28 04:42:52.866949 Train: epoch: 44 batch: 0/4, loss: 0.001588
2023-06-28 04:43:37.419490 Validation: avg loss: 1.4595, avg acc: 60.9524%
2023-06-28 04:43:51.441179 Train: epoch: 45 batch: 0/4, loss: 0.002320
2023-06-28 04:44:36.373416 Validation: avg loss: 1.4140, avg acc: 66.6667%
2023-06-28 04:44:50.575468 Train: epoch: 46 batch: 0/4, loss: 0.001628
2023-06-28 04:45:36.094658 Validation: avg loss: 1.3962, avg acc: 66.6667%
2023-06-28 04:45:50.360514 Train: epoch: 47 batch: 0/4, loss: 0.001421
2023-06-28 04:46:35.518807 Validation: avg loss: 1.3719, avg acc: 64.7619%
2023-06-28 04:46:49.783401 Train: epoch: 48 batch: 0/4, loss: 0.001688
2023-06-28 04:47:34.452598 Validation: avg loss: 1.3766, avg acc: 67.6190%
2023-06-28 04:47:48.920135 Train: epoch: 49 batch: 0/4, loss: 0.001522
2023-06-28 04:48:34.555909 Validation: avg loss: 1.3792, avg acc: 67.6190%
2023-06-28 04:48:48.703559 Train: epoch: 50 batch: 0/4, loss: 0.000546
2023-06-28 04:49:34.074328 Validation: avg loss: 1.3799, avg acc: 67.6190%
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nv_labs=[0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0]
nv_preds=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1]
dcu_labs=[0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0]
dcu_preds=[0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0]
def sig_detection(preds, labs):
tp = 0
fp = 0
tn = 0
fn = 0
for i in range(0, len(preds)):
if preds[i] == 1 and labs[i] == 1:
tp += 1
elif preds[i] == 1 and labs[i] == 0:
fp += 1
elif preds[i] == 0 and labs[i] == 1:
fn += 1
elif preds[i] == 0 and labs[i] == 0:
tn += 1
acc = ((tp + tn) / (tp + fp + fn + tn))
print('accuracy: ', acc)
print('precision: ', tp / (tp + fp))
print('recall: ', tp / (tp + fn))
if __name__ == '__main__':
sig_detection(dcu_preds, dcu_labs)
sig_detection(nv_preds, nv_labs)
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2023-06-27 01:59:32.283091 cuda
2023-06-27 02:00:19.220679 Train: epoch: 1 batch: 0/4, loss: 0.692692
2023-06-27 02:01:45.056517 Validation: avg loss: 0.6880, avg acc: 56.1905%
2023-06-27 02:02:12.228852 Train: epoch: 2 batch: 0/4, loss: 0.658743
2023-06-27 02:03:38.560296 Validation: avg loss: 0.6877, avg acc: 56.1905%
2023-06-27 02:04:05.720454 Train: epoch: 3 batch: 0/4, loss: 0.657871
2023-06-27 02:05:32.169351 Validation: avg loss: 0.6854, avg acc: 56.1905%
2023-06-27 02:05:59.317089 Train: epoch: 4 batch: 0/4, loss: 0.666273
2023-06-27 02:07:25.685815 Validation: avg loss: 0.6877, avg acc: 56.1905%
2023-06-27 02:07:52.791185 Train: epoch: 5 batch: 0/4, loss: 0.645225
2023-06-27 02:09:19.154753 Validation: avg loss: 0.6858, avg acc: 56.1905%
2023-06-27 02:09:46.424731 Train: epoch: 6 batch: 0/4, loss: 0.623888
2023-06-27 02:11:12.928777 Validation: avg loss: 0.6847, avg acc: 56.1905%
2023-06-27 02:11:40.214777 Train: epoch: 7 batch: 0/4, loss: 0.580446
2023-06-27 02:13:06.533535 Validation: avg loss: 0.6864, avg acc: 56.1905%
2023-06-27 02:13:33.676289 Train: epoch: 8 batch: 0/4, loss: 0.567984
2023-06-27 02:15:00.074375 Validation: avg loss: 0.6962, avg acc: 43.8095%
2023-06-27 02:15:27.339969 Train: epoch: 9 batch: 0/4, loss: 0.498715
2023-06-27 02:16:53.824842 Validation: avg loss: 0.7143, avg acc: 43.8095%
2023-06-27 02:17:20.991042 Train: epoch: 10 batch: 0/4, loss: 0.412389
2023-06-27 02:18:47.339751 Validation: avg loss: 0.6938, avg acc: 52.3810%
2023-06-27 02:19:14.535807 Train: epoch: 11 batch: 0/4, loss: 0.431757
2023-06-27 02:20:41.104110 Validation: avg loss: 0.6764, avg acc: 59.0476%
2023-06-27 02:21:08.353358 Train: epoch: 12 batch: 0/4, loss: 0.341980
2023-06-27 02:22:34.747154 Validation: avg loss: 0.6907, avg acc: 55.2381%
2023-06-27 02:23:01.951064 Train: epoch: 13 batch: 0/4, loss: 0.191258
2023-06-27 02:24:28.526972 Validation: avg loss: 0.7229, avg acc: 52.3810%
2023-06-27 02:24:55.693149 Train: epoch: 14 batch: 0/4, loss: 0.197494
2023-06-27 02:26:22.101262 Validation: avg loss: 0.7343, avg acc: 61.9048%
2023-06-27 02:26:49.365855 Train: epoch: 15 batch: 0/4, loss: 0.131904
2023-06-27 02:28:15.880422 Validation: avg loss: 0.8183, avg acc: 60.9524%
2023-06-27 02:28:43.054247 Train: epoch: 16 batch: 0/4, loss: 0.054692
2023-06-27 02:30:09.060479 Validation: avg loss: 0.9153, avg acc: 60.0000%
2023-06-27 02:30:36.137310 Train: epoch: 17 batch: 0/4, loss: 0.050563
2023-06-27 02:32:02.117634 Validation: avg loss: 0.9844, avg acc: 60.9524%
2023-06-27 02:32:29.169731 Train: epoch: 18 batch: 0/4, loss: 0.053166
2023-06-27 02:33:55.137135 Validation: avg loss: 1.0830, avg acc: 60.9524%
2023-06-27 02:34:22.166846 Train: epoch: 19 batch: 0/4, loss: 0.032123
2023-06-27 02:35:48.367994 Validation: avg loss: 1.2188, avg acc: 59.0476%
2023-06-27 02:36:15.464687 Train: epoch: 20 batch: 0/4, loss: 0.011078
2023-06-27 02:37:41.546528 Validation: avg loss: 1.3116, avg acc: 58.0952%
2023-06-27 02:38:08.613742 Train: epoch: 21 batch: 0/4, loss: 0.009437
2023-06-27 02:39:34.745311 Validation: avg loss: 1.3991, avg acc: 60.0000%
2023-06-27 02:40:01.727573 Train: epoch: 22 batch: 0/4, loss: 0.008098
2023-06-27 02:41:27.959071 Validation: avg loss: 1.4563, avg acc: 60.9524%
2023-06-27 02:41:54.947086 Train: epoch: 23 batch: 0/4, loss: 0.005597
2023-06-27 02:43:20.979804 Validation: avg loss: 1.5111, avg acc: 58.0952%
2023-06-27 02:43:48.082680 Train: epoch: 24 batch: 0/4, loss: 0.007035
2023-06-27 02:45:13.880253 Validation: avg loss: 1.5491, avg acc: 58.0952%
2023-06-27 02:45:40.821902 Train: epoch: 25 batch: 0/4, loss: 0.014974
2023-06-27 02:47:06.215747 Validation: avg loss: 1.6083, avg acc: 59.0476%
2023-06-27 02:47:33.049431 Train: epoch: 26 batch: 0/4, loss: 0.009223
2023-06-27 02:48:58.230909 Validation: avg loss: 1.6236, avg acc: 58.0952%
2023-06-27 02:49:25.116434 Train: epoch: 27 batch: 0/4, loss: 0.004450
2023-06-27 02:50:50.249101 Validation: avg loss: 1.6272, avg acc: 60.9524%
2023-06-27 02:51:17.185289 Train: epoch: 28 batch: 0/4, loss: 0.004213
2023-06-27 02:52:42.417771 Validation: avg loss: 1.6348, avg acc: 60.0000%
2023-06-27 02:53:09.392169 Train: epoch: 29 batch: 0/4, loss: 0.007142
2023-06-27 02:54:34.479362 Validation: avg loss: 1.6620, avg acc: 60.0000%
2023-06-27 02:55:01.579140 Train: epoch: 30 batch: 0/4, loss: 0.002469
2023-06-27 02:56:26.774915 Validation: avg loss: 1.6754, avg acc: 58.0952%
2023-06-27 02:56:53.730580 Train: epoch: 31 batch: 0/4, loss: 0.001998
2023-06-27 02:58:18.914844 Validation: avg loss: 1.6636, avg acc: 58.0952%
2023-06-27 02:58:45.838359 Train: epoch: 32 batch: 0/4, loss: 0.004437
2023-06-27 03:00:11.088444 Validation: avg loss: 1.6740, avg acc: 56.1905%
2023-06-27 03:00:38.057626 Train: epoch: 33 batch: 0/4, loss: 0.003358
2023-06-27 03:02:03.267012 Validation: avg loss: 1.6721, avg acc: 58.0952%
2023-06-27 03:02:30.192813 Train: epoch: 34 batch: 0/4, loss: 0.007877
2023-06-27 03:03:55.370666 Validation: avg loss: 1.7256, avg acc: 57.1429%
2023-06-27 03:04:22.330398 Train: epoch: 35 batch: 0/4, loss: 0.002116
2023-06-27 03:05:47.533817 Validation: avg loss: 1.7264, avg acc: 57.1429%
2023-06-27 03:06:14.522816 Train: epoch: 36 batch: 0/4, loss: 0.002035
2023-06-27 03:07:39.757508 Validation: avg loss: 1.7241, avg acc: 58.0952%
2023-06-27 03:08:06.838154 Train: epoch: 37 batch: 0/4, loss: 0.002826
2023-06-27 03:09:31.977029 Validation: avg loss: 1.7297, avg acc: 57.1429%
2023-06-27 03:09:59.061422 Train: epoch: 38 batch: 0/4, loss: 0.003084
2023-06-27 03:11:24.248853 Validation: avg loss: 1.7399, avg acc: 56.1905%
2023-06-27 03:11:51.165653 Train: epoch: 39 batch: 0/4, loss: 0.001958
2023-06-27 03:13:16.253117 Validation: avg loss: 1.7405, avg acc: 57.1429%
2023-06-27 03:13:43.233199 Train: epoch: 40 batch: 0/4, loss: 0.001262
2023-06-27 03:15:08.475732 Validation: avg loss: 1.7326, avg acc: 56.1905%
2023-06-27 03:15:35.474762 Train: epoch: 41 batch: 0/4, loss: 0.001404
2023-06-27 03:17:00.632572 Validation: avg loss: 1.7311, avg acc: 56.1905%
2023-06-27 03:17:27.635665 Train: epoch: 42 batch: 0/4, loss: 0.001106
2023-06-27 03:18:52.923263 Validation: avg loss: 1.7361, avg acc: 56.1905%
2023-06-27 03:19:19.869122 Train: epoch: 43 batch: 0/4, loss: 0.001162
2023-06-27 03:20:44.935747 Validation: avg loss: 1.7377, avg acc: 56.1905%
2023-06-27 03:21:11.870147 Train: epoch: 44 batch: 0/4, loss: 0.001035
2023-06-27 03:22:36.973028 Validation: avg loss: 1.7479, avg acc: 56.1905%
2023-06-27 03:23:03.876731 Train: epoch: 45 batch: 0/4, loss: 0.000927
2023-06-27 03:24:28.880784 Validation: avg loss: 1.7523, avg acc: 57.1429%
2023-06-27 03:24:55.838651 Train: epoch: 46 batch: 0/4, loss: 0.001295
2023-06-27 03:26:20.951645 Validation: avg loss: 1.7522, avg acc: 57.1429%
2023-06-27 03:26:47.910058 Train: epoch: 47 batch: 0/4, loss: 0.000797
2023-06-27 03:28:13.000012 Validation: avg loss: 1.7620, avg acc: 58.0952%
2023-06-27 03:28:40.022628 Train: epoch: 48 batch: 0/4, loss: 0.000734
2023-06-27 03:30:05.314235 Validation: avg loss: 1.7688, avg acc: 57.1429%
2023-06-27 03:30:32.364647 Train: epoch: 49 batch: 0/4, loss: 0.000535
2023-06-27 03:31:57.393118 Validation: avg loss: 1.7718, avg acc: 57.1429%
2023-06-27 03:32:24.461149 Train: epoch: 50 batch: 0/4, loss: 0.000676
2023-06-27 03:33:49.586317 Validation: avg loss: 1.7748, avg acc: 56.1905%
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2023-06-27 03:33:49.620538 cuda
2023-06-27 03:34:16.530640 Train: epoch: 1 batch: 0/4, loss: 0.693080
2023-06-27 03:35:41.806763 Validation: avg loss: 0.6870, avg acc: 58.0952%
2023-06-27 03:36:08.742024 Train: epoch: 2 batch: 0/4, loss: 0.677773
2023-06-27 03:37:34.191173 Validation: avg loss: 0.6835, avg acc: 58.0952%
2023-06-27 03:38:01.231267 Train: epoch: 3 batch: 0/4, loss: 0.673670
2023-06-27 03:39:26.649802 Validation: avg loss: 0.6825, avg acc: 58.0952%
2023-06-27 03:39:53.565106 Train: epoch: 4 batch: 0/4, loss: 0.680140
2023-06-27 03:41:18.969671 Validation: avg loss: 0.6821, avg acc: 58.0952%
2023-06-27 03:41:45.893202 Train: epoch: 5 batch: 0/4, loss: 0.631272
2023-06-27 03:43:11.322387 Validation: avg loss: 0.6810, avg acc: 58.0952%
2023-06-27 03:43:38.272657 Train: epoch: 6 batch: 0/4, loss: 0.608761
2023-06-27 03:45:03.721667 Validation: avg loss: 0.6815, avg acc: 58.0952%
2023-06-27 03:45:30.705582 Train: epoch: 7 batch: 0/4, loss: 0.601036
2023-06-27 03:46:56.087300 Validation: avg loss: 0.6981, avg acc: 58.0952%
2023-06-27 03:47:23.023310 Train: epoch: 8 batch: 0/4, loss: 0.603923
2023-06-27 03:48:48.390601 Validation: avg loss: 0.6952, avg acc: 58.0952%
2023-06-27 03:49:15.382933 Train: epoch: 9 batch: 0/4, loss: 0.470182
2023-06-27 03:50:40.781453 Validation: avg loss: 0.6828, avg acc: 58.0952%
2023-06-27 03:51:07.874860 Train: epoch: 10 batch: 0/4, loss: 0.386110
2023-06-27 03:52:33.215767 Validation: avg loss: 0.7465, avg acc: 58.0952%
2023-06-27 03:53:00.156132 Train: epoch: 11 batch: 0/4, loss: 0.397895
2023-06-27 03:54:25.536366 Validation: avg loss: 0.7050, avg acc: 58.0952%
2023-06-27 03:54:52.469428 Train: epoch: 12 batch: 0/4, loss: 0.262515
2023-06-27 03:56:17.898705 Validation: avg loss: 0.7457, avg acc: 57.1429%
2023-06-27 03:56:44.895663 Train: epoch: 13 batch: 0/4, loss: 0.224342
2023-06-27 03:58:10.393033 Validation: avg loss: 0.7781, avg acc: 63.8095%
2023-06-27 03:58:37.311181 Train: epoch: 14 batch: 0/4, loss: 0.134823
2023-06-27 04:00:02.594674 Validation: avg loss: 0.7340, avg acc: 60.9524%
2023-06-27 04:00:29.503775 Train: epoch: 15 batch: 0/4, loss: 0.126792
2023-06-27 04:01:54.777270 Validation: avg loss: 0.7953, avg acc: 60.9524%
2023-06-27 04:02:21.955920 Train: epoch: 16 batch: 0/4, loss: 0.114732
2023-06-27 04:03:48.180689 Validation: avg loss: 0.9175, avg acc: 62.8571%
2023-06-27 04:04:15.437565 Train: epoch: 17 batch: 0/4, loss: 0.040243
2023-06-27 04:05:41.646578 Validation: avg loss: 1.0110, avg acc: 59.0476%
2023-06-27 04:06:08.802477 Train: epoch: 18 batch: 0/4, loss: 0.029593
2023-06-27 04:07:34.864507 Validation: avg loss: 0.9962, avg acc: 60.0000%
2023-06-27 04:08:02.075055 Train: epoch: 19 batch: 0/4, loss: 0.019918
2023-06-27 04:09:28.146785 Validation: avg loss: 1.0330, avg acc: 61.9048%
2023-06-27 04:09:55.344057 Train: epoch: 20 batch: 0/4, loss: 0.013042
2023-06-27 04:11:21.367763 Validation: avg loss: 1.1118, avg acc: 60.9524%
2023-06-27 04:11:48.596764 Train: epoch: 21 batch: 0/4, loss: 0.029912
2023-06-27 04:13:14.732747 Validation: avg loss: 1.2075, avg acc: 60.0000%
2023-06-27 04:13:41.908463 Train: epoch: 22 batch: 0/4, loss: 0.018193
2023-06-27 04:15:07.903724 Validation: avg loss: 1.2723, avg acc: 63.8095%
2023-06-27 04:15:35.079332 Train: epoch: 23 batch: 0/4, loss: 0.012390
2023-06-27 04:17:01.043754 Validation: avg loss: 1.2906, avg acc: 61.9048%
2023-06-27 04:17:28.257118 Train: epoch: 24 batch: 0/4, loss: 0.007024
2023-06-27 04:18:54.271198 Validation: avg loss: 1.3381, avg acc: 62.8571%
2023-06-27 04:19:21.468763 Train: epoch: 25 batch: 0/4, loss: 0.014566
2023-06-27 04:20:47.772050 Validation: avg loss: 1.4036, avg acc: 61.9048%
2023-06-27 04:21:15.024444 Train: epoch: 26 batch: 0/4, loss: 0.003964
2023-06-27 04:22:41.461127 Validation: avg loss: 1.4325, avg acc: 62.8571%
2023-06-27 04:23:08.790864 Train: epoch: 27 batch: 0/4, loss: 0.004086
2023-06-27 04:24:35.101757 Validation: avg loss: 1.4324, avg acc: 61.9048%
2023-06-27 04:25:02.417699 Train: epoch: 28 batch: 0/4, loss: 0.008229
2023-06-27 04:26:28.885759 Validation: avg loss: 1.4633, avg acc: 61.9048%
2023-06-27 04:26:56.150808 Train: epoch: 29 batch: 0/4, loss: 0.008524
2023-06-27 04:28:22.438783 Validation: avg loss: 1.5122, avg acc: 60.9524%
2023-06-27 04:28:49.761823 Train: epoch: 30 batch: 0/4, loss: 0.004061
2023-06-27 04:30:16.127859 Validation: avg loss: 1.5431, avg acc: 60.9524%
2023-06-27 04:30:43.331688 Train: epoch: 31 batch: 0/4, loss: 0.007024
2023-06-27 04:32:09.483676 Validation: avg loss: 1.5391, avg acc: 61.9048%
2023-06-27 04:32:36.662683 Train: epoch: 32 batch: 0/4, loss: 0.002247
2023-06-27 04:34:02.664147 Validation: avg loss: 1.5419, avg acc: 61.9048%
2023-06-27 04:34:29.873437 Train: epoch: 33 batch: 0/4, loss: 0.002192
2023-06-27 04:35:55.931510 Validation: avg loss: 1.5424, avg acc: 60.9524%
2023-06-27 04:36:23.090882 Train: epoch: 34 batch: 0/4, loss: 0.001588
2023-06-27 04:37:49.141448 Validation: avg loss: 1.5662, avg acc: 60.9524%
2023-06-27 04:38:16.305874 Train: epoch: 35 batch: 0/4, loss: 0.001755
2023-06-27 04:39:42.380459 Validation: avg loss: 1.5682, avg acc: 60.9524%
2023-06-27 04:40:09.618126 Train: epoch: 36 batch: 0/4, loss: 0.001248
2023-06-27 04:41:35.701780 Validation: avg loss: 1.5553, avg acc: 60.9524%
2023-06-27 04:42:02.954181 Train: epoch: 37 batch: 0/4, loss: 0.001146
2023-06-27 04:43:29.042723 Validation: avg loss: 1.5398, avg acc: 61.9048%
2023-06-27 04:43:56.233850 Train: epoch: 38 batch: 0/4, loss: 0.001596
2023-06-27 04:45:22.520642 Validation: avg loss: 1.5308, avg acc: 61.9048%
2023-06-27 04:45:49.661671 Train: epoch: 39 batch: 0/4, loss: 0.003090
2023-06-27 04:47:15.754206 Validation: avg loss: 1.5261, avg acc: 62.8571%
2023-06-27 04:47:42.930905 Train: epoch: 40 batch: 0/4, loss: 0.001614
2023-06-27 04:49:09.075421 Validation: avg loss: 1.5337, avg acc: 62.8571%
2023-06-27 04:49:36.347485 Train: epoch: 41 batch: 0/4, loss: 0.001224
2023-06-27 04:51:02.533730 Validation: avg loss: 1.5332, avg acc: 60.9524%
2023-06-27 04:51:29.771878 Train: epoch: 42 batch: 0/4, loss: 0.001751
2023-06-27 04:52:55.889034 Validation: avg loss: 1.5426, avg acc: 60.0000%
2023-06-27 04:53:23.095702 Train: epoch: 43 batch: 0/4, loss: 0.000911
2023-06-27 04:54:49.249387 Validation: avg loss: 1.5437, avg acc: 60.0000%
2023-06-27 04:55:16.344639 Train: epoch: 44 batch: 0/4, loss: 0.000856
2023-06-27 04:56:42.462039 Validation: avg loss: 1.5554, avg acc: 60.0000%
2023-06-27 04:57:09.636866 Train: epoch: 45 batch: 0/4, loss: 0.000915
2023-06-27 04:58:35.815228 Validation: avg loss: 1.5604, avg acc: 60.9524%
2023-06-27 04:59:02.971681 Train: epoch: 46 batch: 0/4, loss: 0.000812
2023-06-27 05:00:29.061047 Validation: avg loss: 1.5614, avg acc: 60.9524%
2023-06-27 05:00:56.206586 Train: epoch: 47 batch: 0/4, loss: 0.000677
2023-06-27 05:02:22.282724 Validation: avg loss: 1.5675, avg acc: 61.9048%
2023-06-27 05:02:49.470639 Train: epoch: 48 batch: 0/4, loss: 0.000872
2023-06-27 05:04:15.620676 Validation: avg loss: 1.5682, avg acc: 60.9524%
2023-06-27 05:04:42.754795 Train: epoch: 49 batch: 0/4, loss: 0.000688
2023-06-27 05:06:08.739963 Validation: avg loss: 1.5752, avg acc: 61.9048%
2023-06-27 05:06:35.853730 Train: epoch: 50 batch: 0/4, loss: 0.000888
2023-06-27 05:08:01.872036 Validation: avg loss: 1.5715, avg acc: 61.9048%
results/dcu/230627050801.log 0 → 100644
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results/dcu/230627064222.log 0 → 100644
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