When training the Alexnet for at least 5 epochs, there is no improvement after epoch number 3 regarding to  precision (Prec) and Loss (see output below).
What’s the reason for that, and how can I fix that?
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============================
Start training epoch: 1/50
@ 2018-12-20 17:35:18.972483
============================
Epoch: [1][0/354]	Time 2.656 (2.656)	Data 0.117 (0.117)	Loss 3.7634 (3.7634)	Prec@1 2.000 (2.000)	Prec@5 9.000 (9.000)
Epoch: [1][10/354]	Time 2.016 (2.083)	Data 0.003 (0.013)	Loss 3.5828 (3.6961)	Prec@1 6.000 (6.182)	Prec@5 29.000 (28.091)
Epoch: [1][20/354]	Time 2.015 (2.056)	Data 0.002 (0.008)	Loss 3.4318 (3.6030)	Prec@1 8.000 (6.000)	Prec@5 25.000 (27.476)
Epoch: [1][30/354]	Time 2.042 (2.046)	Data 0.003 (0.006)	Loss 3.5251 (3.5495)	Prec@1 4.000 (5.935)	Prec@5 16.000 (27.548)
Epoch: [1][40/354]	Time 1.953 (2.037)	Data 0.003 (0.005)	Loss 3.3811 (3.5167)	Prec@1 5.000 (6.073)	Prec@5 31.000 (27.634)
Epoch: [1][50/354]	Time 1.951 (2.031)	Data 0.003 (0.005)	Loss 3.4218 (3.4937)	Prec@1 5.000 (5.843)	Prec@5 26.000 (27.255)
Epoch: [1][60/354]	Time 1.968 (2.025)	Data 0.003 (0.005)	Loss 3.4051 (3.4816)	Prec@1 11.000 (6.033)	Prec@5 29.000 (27.836)
Epoch: [1][70/354]	Time 2.045 (2.024)	Data 0.003 (0.004)	Loss 3.3996 (3.4749)	Prec@1 7.000 (6.113)	Prec@5 26.000 (27.986)
Epoch: [1][80/354]	Time 1.985 (2.021)	Data 0.010 (0.004)	Loss 3.3590 (3.4666)	Prec@1 7.000 (6.049)	Prec@5 35.000 (28.346)
Epoch: [1][90/354]	Time 1.993 (2.018)	Data 0.002 (0.004)	Loss 3.4126 (3.4599)	Prec@1 10.000 (6.165)	Prec@5 30.000 (28.582)
Epoch: [1][100/354]	Time 1.957 (2.016)	Data 0.002 (0.004)	Loss 3.3853 (3.4548)	Prec@1 6.000 (6.218)	Prec@5 36.000 (29.168)
Epoch: [1][110/354]	Time 2.045 (2.015)	Data 0.003 (0.004)	Loss 3.4590 (3.4529)	Prec@1 6.000 (6.234)	Prec@5 23.000 (29.171)
Epoch: [1][120/354]	Time 2.006 (2.017)	Data 0.002 (0.004)	Loss 3.4032 (3.4459)	Prec@1 9.000 (6.479)	Prec@5 31.000 (29.347)
Epoch: [1][130/354]	Time 1.966 (2.019)	Data 0.002 (0.004)	Loss 3.2392 (3.4405)	Prec@1 12.000 (6.634)	Prec@5 40.000 (29.733)
Epoch: [1][140/354]	Time 2.038 (2.020)	Data 0.002 (0.004)	Loss 3.0439 (3.4248)	Prec@1 22.000 (7.085)	Prec@5 42.000 (30.255)
Epoch: [1][150/354]	Time 1.932 (2.020)	Data 0.003 (0.004)	Loss 3.2785 (3.4144)	Prec@1 11.000 (7.258)	Prec@5 36.000 (30.616)
Epoch: [1][160/354]	Time 1.982 (2.020)	Data 0.002 (0.003)	Loss 3.2526 (3.4095)	Prec@1 14.000 (7.429)	Prec@5 37.000 (30.652)
Epoch: [1][170/354]	Time 1.991 (2.020)	Data 0.003 (0.003)	Loss 3.6547 (3.4247)	Prec@1 8.000 (7.480)	Prec@5 28.000 (30.702)
Epoch: [1][180/354]	Time 2.007 (2.020)	Data 0.003 (0.003)	Loss 3.5979 (3.4333)	Prec@1 4.000 (7.414)	Prec@5 20.000 (30.652)
Epoch: [1][190/354]	Time 2.035 (2.020)	Data 0.003 (0.004)	Loss 3.3323 (3.4335)	Prec@1 5.000 (7.319)	Prec@5 30.000 (30.628)
Epoch: [1][200/354]	Time 1.991 (2.019)	Data 0.003 (0.003)	Loss 3.2653 (3.4328)	Prec@1 7.000 (7.308)	Prec@5 40.000 (30.622)
Epoch: [1][210/354]	Time 1.983 (2.018)	Data 0.003 (0.003)	Loss 3.5182 (3.4308)	Prec@1 6.000 (7.270)	Prec@5 26.000 (30.640)
Epoch: [1][220/354]	Time 1.917 (2.016)	Data 0.002 (0.003)	Loss 3.4520 (3.4308)	Prec@1 5.000 (7.199)	Prec@5 25.000 (30.606)
Epoch: [1][230/354]	Time 1.929 (2.016)	Data 0.002 (0.003)	Loss 3.4194 (3.4295)	Prec@1 8.000 (7.177)	Prec@5 28.000 (30.693)
Epoch: [1][240/354]	Time 2.056 (2.016)	Data 0.007 (0.003)	Loss 3.4955 (3.4287)	Prec@1 5.000 (7.237)	Prec@5 35.000 (30.842)
Epoch: [1][250/354]	Time 2.075 (2.016)	Data 0.003 (0.003)	Loss 3.4167 (3.4313)	Prec@1 7.000 (7.167)	Prec@5 29.000 (30.677)
Epoch: [1][260/354]	Time 2.010 (2.017)	Data 0.003 (0.003)	Loss 3.3218 (3.4298)	Prec@1 4.000 (7.169)	Prec@5 34.000 (30.759)
Epoch: [1][270/354]	Time 2.085 (2.016)	Data 0.003 (0.003)	Loss 3.3360 (3.4284)	Prec@1 8.000 (7.229)	Prec@5 37.000 (30.790)
Epoch: [1][280/354]	Time 1.927 (2.016)	Data 0.003 (0.003)	Loss 3.3796 (3.4276)	Prec@1 9.000 (7.256)	Prec@5 34.000 (30.879)
Epoch: [1][290/354]	Time 2.109 (2.015)	Data 0.003 (0.003)	Loss 3.4033 (3.4256)	Prec@1 9.000 (7.278)	Prec@5 39.000 (31.000)
Epoch: [1][300/354]	Time 2.023 (2.016)	Data 0.003 (0.003)	Loss 3.5021 (3.4245)	Prec@1 3.000 (7.243)	Prec@5 22.000 (31.023)
Epoch: [1][310/354]	Time 1.976 (2.016)	Data 0.002 (0.003)	Loss 3.4964 (3.4228)	Prec@1 5.000 (7.344)	Prec@5 33.000 (31.151)
Epoch: [1][320/354]	Time 1.933 (2.016)	Data 0.003 (0.003)	Loss 3.4231 (3.4205)	Prec@1 5.000 (7.393)	Prec@5 31.000 (31.343)
Epoch: [1][330/354]	Time 1.955 (2.016)	Data 0.003 (0.003)	Loss 3.3093 (3.4203)	Prec@1 11.000 (7.423)	Prec@5 32.000 (31.332)
Epoch: [1][340/354]	Time 2.002 (2.015)	Data 0.003 (0.003)	Loss 3.4843 (3.4185)	Prec@1 6.000 (7.449)	Prec@5 31.000 (31.455)
Epoch: [1][350/354]	Time 2.020 (2.015)	Data 0.003 (0.003)	Loss 3.3400 (3.4170)	Prec@1 3.000 (7.473)	Prec@5 28.000 (31.467)
/var/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:79: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
/var/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:80: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
Test: [0/39]	Time 0.836 (0.836)	Loss 4.1514 (4.1514)	Prec@1 1.000 (1.000)	Prec@5 7.000 (7.000)
Test: [10/39]	Time 0.613 (0.599)	Loss 4.1281 (4.0848)	Prec@1 1.000 (2.727)	Prec@5 9.000 (12.273)
Test: [20/39]	Time 0.553 (0.581)	Loss 4.0871 (4.0643)	Prec@1 1.000 (2.952)	Prec@5 9.000 (12.286)
Test: [30/39]	Time 0.580 (0.577)	Loss 4.1038 (4.0520)	Prec@1 2.000 (2.935)	Prec@5 7.000 (12.516)
Test: [30/39]	Time 0.580 (0.577)	Loss 4.1038 (4.0520)	Prec@1 2.000 (2.935)	Prec@5 7.000 (12.516)

=============================
Finished training epoch: 1/50
@ 2018-12-20 17:47:33.444769
=============================

============================
Start training epoch: 2/50
@ 2018-12-20 17:47:33.445010
============================
Epoch: [2][0/354]	Time 2.733 (2.733)	Data 0.116 (0.116)	Loss 3.3509 (3.3509)	Prec@1 11.000 (11.000)	Prec@5 36.000 (36.000)
Epoch: [2][10/354]	Time 2.082 (2.110)	Data 0.004 (0.012)	Loss 3.3165 (3.3353)	Prec@1 7.000 (9.273)	Prec@5 38.000 (35.727)
Epoch: [2][20/354]	Time 2.057 (2.073)	Data 0.003 (0.008)	Loss 3.2800 (3.3334)	Prec@1 9.000 (9.476)	Prec@5 33.000 (35.571)
Epoch: [2][30/354]	Time 1.999 (2.052)	Data 0.002 (0.006)	Loss 3.4280 (3.3352)	Prec@1 7.000 (9.806)	Prec@5 30.000 (35.258)
Epoch: [2][40/354]	Time 2.083 (2.045)	Data 0.003 (0.005)	Loss 3.2260 (3.3332)	Prec@1 7.000 (9.902)	Prec@5 41.000 (35.780)
Epoch: [2][50/354]	Time 2.124 (2.045)	Data 0.002 (0.005)	Loss 3.2208 (3.3331)	Prec@1 9.000 (9.824)	Prec@5 38.000 (35.588)
Epoch: [2][60/354]	Time 2.002 (2.039)	Data 0.003 (0.004)	Loss 3.3373 (3.3206)	Prec@1 10.000 (10.049)	Prec@5 33.000 (36.230)
Epoch: [2][70/354]	Time 1.993 (2.040)	Data 0.003 (0.004)	Loss 3.3383 (3.3254)	Prec@1 7.000 (10.042)	Prec@5 33.000 (36.042)
Epoch: [2][80/354]	Time 1.957 (2.042)	Data 0.003 (0.004)	Loss 3.4124 (3.3293)	Prec@1 7.000 (9.840)	Prec@5 32.000 (35.852)
Epoch: [2][90/354]	Time 2.075 (2.041)	Data 0.003 (0.004)	Loss 3.2135 (3.3235)	Prec@1 15.000 (9.813)	Prec@5 37.000 (35.758)
Epoch: [2][100/354]	Time 2.044 (2.039)	Data 0.003 (0.004)	Loss 3.2491 (3.3171)	Prec@1 13.000 (9.921)	Prec@5 40.000 (36.168)
Epoch: [2][110/354]	Time 2.050 (2.036)	Data 0.003 (0.004)	Loss 3.2031 (3.3128)	Prec@1 14.000 (10.117)	Prec@5 37.000 (36.432)
Epoch: [2][120/354]	Time 2.009 (2.035)	Data 0.003 (0.004)	Loss 3.2368 (3.3022)	Prec@1 12.000 (10.289)	Prec@5 38.000 (36.736)
Epoch: [2][130/354]	Time 1.972 (2.037)	Data 0.003 (0.004)	Loss 3.2186 (3.2908)	Prec@1 13.000 (10.573)	Prec@5 40.000 (37.214)
Epoch: [2][140/354]	Time 2.039 (2.035)	Data 0.003 (0.004)	Loss 3.2031 (3.2808)	Prec@1 16.000 (10.787)	Prec@5 42.000 (37.730)
Epoch: [2][150/354]	Time 2.081 (2.036)	Data 0.003 (0.004)	Loss 3.0879 (3.2682)	Prec@1 20.000 (11.113)	Prec@5 41.000 (38.245)
Epoch: [2][160/354]	Time 2.059 (2.036)	Data 0.003 (0.004)	Loss 3.0897 (3.2672)	Prec@1 12.000 (11.099)	Prec@5 49.000 (38.373)
Epoch: [2][170/354]	Time 1.978 (2.037)	Data 0.002 (0.004)	Loss 3.2178 (3.2681)	Prec@1 13.000 (11.082)	Prec@5 41.000 (38.415)
Epoch: [2][180/354]	Time 2.146 (2.037)	Data 0.002 (0.004)	Loss 3.3955 (3.2656)	Prec@1 6.000 (11.105)	Prec@5 36.000 (38.619)
Epoch: [2][190/354]	Time 1.984 (2.039)	Data 0.002 (0.003)	Loss 3.1231 (3.2643)	Prec@1 14.000 (11.068)	Prec@5 42.000 (38.754)
Epoch: [2][200/354]	Time 1.999 (2.038)	Data 0.003 (0.003)	Loss 3.4245 (3.2615)	Prec@1 7.000 (11.025)	Prec@5 36.000 (38.816)
Epoch: [2][210/354]	Time 1.991 (2.038)	Data 0.002 (0.003)	Loss 3.1295 (3.2606)	Prec@1 15.000 (11.028)	Prec@5 45.000 (38.801)
Epoch: [2][220/354]	Time 1.955 (2.038)	Data 0.004 (0.003)	Loss 3.1918 (3.2598)	Prec@1 9.000 (11.054)	Prec@5 32.000 (38.647)
Epoch: [2][230/354]	Time 2.017 (2.038)	Data 0.003 (0.003)	Loss 2.9778 (3.2553)	Prec@1 17.000 (11.117)	Prec@5 48.000 (38.866)
Epoch: [2][240/354]	Time 2.133 (2.038)	Data 0.003 (0.003)	Loss 2.9836 (3.2488)	Prec@1 16.000 (11.253)	Prec@5 45.000 (39.100)
Epoch: [2][250/354]	Time 2.017 (2.039)	Data 0.002 (0.003)	Loss 2.9704 (3.2396)	Prec@1 12.000 (11.434)	Prec@5 42.000 (39.446)
Epoch: [2][260/354]	Time 1.989 (2.038)	Data 0.003 (0.003)	Loss 3.0747 (3.2383)	Prec@1 12.000 (11.479)	Prec@5 46.000 (39.460)
Epoch: [2][270/354]	Time 2.014 (2.037)	Data 0.003 (0.003)	Loss 2.8776 (3.2337)	Prec@1 17.000 (11.550)	Prec@5 50.000 (39.598)
Epoch: [2][280/354]	Time 2.015 (2.038)	Data 0.003 (0.003)	Loss 3.1961 (3.2312)	Prec@1 13.000 (11.548)	Prec@5 38.000 (39.662)
Epoch: [2][290/354]	Time 2.080 (2.038)	Data 0.007 (0.003)	Loss 3.1483 (3.2254)	Prec@1 12.000 (11.660)	Prec@5 43.000 (39.808)
Epoch: [2][300/354]	Time 2.035 (2.037)	Data 0.003 (0.003)	Loss 2.6297 (3.2115)	Prec@1 26.000 (11.927)	Prec@5 63.000 (40.279)
Epoch: [2][310/354]	Time 2.058 (2.039)	Data 0.003 (0.003)	Loss 2.5613 (3.1940)	Prec@1 22.000 (12.209)	Prec@5 63.000 (40.955)
Epoch: [2][320/354]	Time 2.060 (2.039)	Data 0.003 (0.003)	Loss 2.7152 (3.1785)	Prec@1 14.000 (12.421)	Prec@5 60.000 (41.551)
Epoch: [2][330/354]	Time 2.106 (2.039)	Data 0.003 (0.003)	Loss 2.6099 (3.1671)	Prec@1 20.000 (12.610)	Prec@5 59.000 (41.952)
Epoch: [2][340/354]	Time 2.015 (2.040)	Data 0.003 (0.003)	Loss 2.4312 (3.1535)	Prec@1 30.000 (12.792)	Prec@5 65.000 (42.372)
Epoch: [2][350/354]	Time 2.025 (2.039)	Data 0.002 (0.003)	Loss 2.5318 (3.1342)	Prec@1 28.000 (13.157)	Prec@5 65.000 (43.020)
Test: [0/39]	Time 0.750 (0.750)	Loss 3.6744 (3.6744)	Prec@1 5.000 (5.000)	Prec@5 32.000 (32.000)
Test: [10/39]	Time 0.588 (0.589)	Loss 3.4847 (3.5892)	Prec@1 6.000 (6.727)	Prec@5 36.000 (33.364)
Test: [20/39]	Time 0.579 (0.576)	Loss 3.6747 (3.5878)	Prec@1 6.000 (6.905)	Prec@5 28.000 (33.190)
Test: [30/39]	Time 0.542 (0.571)	Loss 3.5044 (3.6157)	Prec@1 9.000 (6.903)	Prec@5 32.000 (32.065)
Test: [30/39]	Time 0.542 (0.571)	Loss 3.5044 (3.6157)	Prec@1 9.000 (6.903)	Prec@5 32.000 (32.065)

=============================
Finished training epoch: 2/50
@ 2018-12-20 17:59:56.274658
=============================

============================
Start training epoch: 3/50
@ 2018-12-20 17:59:56.275210
============================
Epoch: [3][0/354]	Time 2.978 (2.978)	Data 0.121 (0.121)	Loss 2.7746 (2.7746)	Prec@1 23.000 (23.000)	Prec@5 61.000 (61.000)
Epoch: [3][10/354]	Time 1.951 (2.118)	Data 0.003 (0.013)	Loss 2.5009 (2.4301)	Prec@1 29.000 (26.727)	Prec@5 68.000 (65.727)
Epoch: [3][20/354]	Time 1.995 (2.071)	Data 0.003 (0.008)	Loss 2.3893 (2.4799)	Prec@1 23.000 (25.333)	Prec@5 66.000 (63.524)
Epoch: [3][30/354]	Time 2.040 (2.056)	Data 0.004 (0.007)	Loss 2.4988 (2.4649)	Prec@1 23.000 (24.968)	Prec@5 67.000 (64.032)
Epoch: [3][40/354]	Time 1.990 (2.057)	Data 0.002 (0.006)	Loss 2.6619 (2.4722)	Prec@1 21.000 (24.585)	Prec@5 60.000 (63.951)
Epoch: [3][50/354]	Time 2.004 (2.057)	Data 0.003 (0.005)	Loss 2.7971 (2.4965)	Prec@1 18.000 (23.922)	Prec@5 61.000 (63.392)
Epoch: [3][60/354]	Time 2.009 (2.056)	Data 0.002 (0.005)	Loss 2.7433 (2.5334)	Prec@1 19.000 (23.213)	Prec@5 53.000 (62.541)
Epoch: [3][70/354]	Time 2.009 (2.051)	Data 0.003 (0.004)	Loss 2.7221 (2.5770)	Prec@1 20.000 (22.676)	Prec@5 57.000 (61.549)
Epoch: [3][80/354]	Time 1.989 (2.048)	Data 0.003 (0.004)	Loss 2.8521 (2.6038)	Prec@1 20.000 (22.148)	Prec@5 60.000 (60.852)
Epoch: [3][90/354]	Time 1.987 (2.043)	Data 0.003 (0.004)	Loss 2.2585 (2.6065)	Prec@1 29.000 (22.176)	Prec@5 72.000 (60.681)
Epoch: [3][100/354]	Time 2.052 (2.044)	Data 0.004 (0.004)	Loss 2.9009 (2.6191)	Prec@1 12.000 (22.089)	Prec@5 50.000 (60.317)
Epoch: [3][110/354]	Time 2.120 (2.041)	Data 0.002 (0.004)	Loss 2.4650 (2.6159)	Prec@1 24.000 (22.252)	Prec@5 62.000 (60.234)
Epoch: [3][120/354]	Time 1.989 (2.046)	Data 0.003 (0.004)	Loss 2.5185 (2.6079)	Prec@1 17.000 (22.306)	Prec@5 57.000 (60.281)
Epoch: [3][130/354]	Time 1.997 (2.043)	Data 0.003 (0.004)	Loss 2.3207 (2.5905)	Prec@1 29.000 (22.504)	Prec@5 72.000 (60.687)
Epoch: [3][140/354]	Time 2.006 (2.039)	Data 0.003 (0.004)	Loss 2.3463 (2.5736)	Prec@1 28.000 (22.887)	Prec@5 69.000 (61.227)
Epoch: [3][150/354]	Time 1.980 (2.038)	Data 0.003 (0.004)	Loss 1.9706 (2.5519)	Prec@1 38.000 (23.358)	Prec@5 78.000 (61.801)
Epoch: [3][160/354]	Time 2.034 (2.036)	Data 0.003 (0.004)	Loss 3.4425 (2.6219)	Prec@1 10.000 (22.857)	Prec@5 30.000 (60.571)
Epoch: [3][170/354]	Time 2.037 (2.034)	Data 0.002 (0.004)	Loss 3.3483 (2.6664)	Prec@1 12.000 (22.053)	Prec@5 34.000 (58.901)
Epoch: [3][180/354]	Time 1.989 (2.033)	Data 0.003 (0.004)	Loss 114.0475 (3.3504)	Prec@1 4.000 (21.309)	Prec@5 17.000 (57.343)
Epoch: [3][190/354]	Time 2.004 (2.034)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (20.288)	Prec@5 16.000 (55.220)
Epoch: [3][200/354]	Time 2.011 (2.033)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 1.000 (19.328)	Prec@5 19.000 (53.239)
Epoch: [3][210/354]	Time 1.935 (2.032)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (18.460)	Prec@5 12.000 (51.384)
Epoch: [3][220/354]	Time 2.052 (2.031)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 2.000 (17.661)	Prec@5 16.000 (49.715)
Epoch: [3][230/354]	Time 1.963 (2.030)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 2.000 (16.957)	Prec@5 25.000 (48.277)
Epoch: [3][240/354]	Time 2.075 (2.030)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (16.282)	Prec@5 10.000 (46.780)
Epoch: [3][250/354]	Time 2.097 (2.030)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (15.677)	Prec@5 13.000 (45.430)
Epoch: [3][260/354]	Time 1.959 (2.029)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (15.111)	Prec@5 13.000 (44.307)
Epoch: [3][270/354]	Time 1.959 (2.028)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (14.587)	Prec@5 16.000 (43.255)
Epoch: [3][280/354]	Time 1.998 (2.027)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (14.089)	Prec@5 14.000 (42.171)
Epoch: [3][290/354]	Time 2.027 (2.027)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (13.663)	Prec@5 13.000 (41.289)
Epoch: [3][300/354]	Time 2.027 (2.028)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (13.229)	Prec@5 12.000 (40.415)
Epoch: [3][310/354]	Time 2.022 (2.027)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 3.000 (12.836)	Prec@5 20.000 (39.553)
Epoch: [3][320/354]	Time 2.032 (2.027)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (12.458)	Prec@5 21.000 (38.816)
Epoch: [3][330/354]	Time 1.961 (2.027)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 1.000 (12.112)	Prec@5 16.000 (38.088)
Epoch: [3][340/354]	Time 1.983 (2.026)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 0.000 (11.777)	Prec@5 13.000 (37.346)
Epoch: [3][350/354]	Time 2.141 (2.025)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (11.467)	Prec@5 20.000 (36.752)
Test: [0/39]	Time 0.797 (0.797)	Loss nan (nan)	Prec@1 1.000 (1.000)	Prec@5 17.000 (17.000)
Test: [10/39]	Time 0.602 (0.604)	Loss nan (nan)	Prec@1 3.000 (2.182)	Prec@5 13.000 (12.727)
Test: [20/39]	Time 0.532 (0.579)	Loss nan (nan)	Prec@1 2.000 (2.000)	Prec@5 12.000 (11.714)
Test: [30/39]	Time 0.542 (0.579)	Loss nan (nan)	Prec@1 3.000 (2.161)	Prec@5 9.000 (11.452)
Test: [30/39]	Time 0.542 (0.579)	Loss nan (nan)	Prec@1 3.000 (2.161)	Prec@5 9.000 (11.452)

=============================
Finished training epoch: 3/50
@ 2018-12-20 18:12:14.660602
=============================

============================
Start training epoch: 4/50
@ 2018-12-20 18:12:14.660861
============================
Epoch: [4][0/354]	Time 2.878 (2.878)	Data 0.147 (0.147)	Loss nan (nan)	Prec@1 0.000 (0.000)	Prec@5 15.000 (15.000)
Epoch: [4][10/354]	Time 2.057 (2.095)	Data 0.003 (0.016)	Loss nan (nan)	Prec@1 1.000 (0.636)	Prec@5 13.000 (14.636)
Epoch: [4][20/354]	Time 2.161 (2.067)	Data 0.007 (0.010)	Loss nan (nan)	Prec@1 1.000 (0.905)	Prec@5 14.000 (14.238)
Epoch: [4][30/354]	Time 2.028 (2.066)	Data 0.003 (0.008)	Loss nan (nan)	Prec@1 2.000 (1.032)	Prec@5 18.000 (14.548)
Epoch: [4][40/354]	Time 1.966 (2.052)	Data 0.002 (0.007)	Loss nan (nan)	Prec@1 1.000 (0.976)	Prec@5 14.000 (14.488)
Epoch: [4][50/354]	Time 1.936 (2.050)	Data 0.002 (0.006)	Loss nan (nan)	Prec@1 0.000 (0.961)	Prec@5 13.000 (14.275)
Epoch: [4][60/354]	Time 2.012 (2.043)	Data 0.003 (0.006)	Loss nan (nan)	Prec@1 1.000 (0.902)	Prec@5 15.000 (14.328)
Epoch: [4][70/354]	Time 2.042 (2.037)	Data 0.002 (0.005)	Loss nan (nan)	Prec@1 0.000 (0.887)	Prec@5 15.000 (14.648)
Epoch: [4][80/354]	Time 1.925 (2.034)	Data 0.003 (0.005)	Loss nan (nan)	Prec@1 2.000 (0.901)	Prec@5 11.000 (14.679)
Epoch: [4][90/354]	Time 1.960 (2.035)	Data 0.003 (0.005)	Loss nan (nan)	Prec@1 2.000 (0.923)	Prec@5 15.000 (14.593)
Epoch: [4][100/354]	Time 2.047 (2.031)	Data 0.003 (0.005)	Loss nan (nan)	Prec@1 0.000 (0.931)	Prec@5 14.000 (14.713)
Epoch: [4][110/354]	Time 1.962 (2.029)	Data 0.003 (0.005)	Loss nan (nan)	Prec@1 0.000 (0.928)	Prec@5 14.000 (14.829)
Epoch: [4][120/354]	Time 2.016 (2.028)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.950)	Prec@5 14.000 (14.901)
Epoch: [4][130/354]	Time 2.045 (2.025)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.947)	Prec@5 21.000 (14.908)
Epoch: [4][140/354]	Time 2.018 (2.026)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 3.000 (0.950)	Prec@5 20.000 (14.894)
Epoch: [4][150/354]	Time 1.992 (2.024)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.927)	Prec@5 13.000 (14.874)
Epoch: [4][160/354]	Time 1.991 (2.023)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.944)	Prec@5 15.000 (14.870)
Epoch: [4][170/354]	Time 2.092 (2.024)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.936)	Prec@5 20.000 (14.895)
Epoch: [4][180/354]	Time 2.054 (2.024)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.923)	Prec@5 18.000 (14.873)
Epoch: [4][190/354]	Time 1.997 (2.026)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.916)	Prec@5 7.000 (14.791)
Epoch: [4][200/354]	Time 2.082 (2.027)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.910)	Prec@5 19.000 (14.856)
Epoch: [4][210/354]	Time 1.929 (2.028)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.915)	Prec@5 17.000 (14.754)
Epoch: [4][220/354]	Time 1.940 (2.028)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 2.000 (0.896)	Prec@5 16.000 (14.792)
Epoch: [4][230/354]	Time 2.049 (2.032)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.870)	Prec@5 15.000 (14.879)
Epoch: [4][240/354]	Time 2.064 (2.033)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.871)	Prec@5 12.000 (14.880)
Epoch: [4][250/354]	Time 1.998 (2.035)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.880)	Prec@5 12.000 (14.865)
Epoch: [4][260/354]	Time 2.056 (2.036)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 2.000 (0.885)	Prec@5 17.000 (14.839)
Epoch: [4][270/354]	Time 2.106 (2.036)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.889)	Prec@5 26.000 (14.867)
Epoch: [4][280/354]	Time 2.130 (2.036)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.904)	Prec@5 10.000 (14.886)
Epoch: [4][290/354]	Time 1.984 (2.037)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.893)	Prec@5 11.000 (14.797)
Epoch: [4][300/354]	Time 2.053 (2.040)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.890)	Prec@5 15.000 (14.834)
Epoch: [4][310/354]	Time 2.077 (2.039)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.904)	Prec@5 16.000 (14.871)
Epoch: [4][320/354]	Time 2.017 (2.037)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.897)	Prec@5 13.000 (14.872)
Epoch: [4][330/354]	Time 1.972 (2.037)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.897)	Prec@5 12.000 (14.846)
Epoch: [4][340/354]	Time 2.011 (2.036)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 4.000 (0.927)	Prec@5 15.000 (14.839)
Epoch: [4][350/354]	Time 2.037 (2.037)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.932)	Prec@5 16.000 (14.832)
Test: [0/39]	Time 0.877 (0.877)	Loss nan (nan)	Prec@1 2.000 (2.000)	Prec@5 6.000 (6.000)
Test: [10/39]	Time 0.523 (0.598)	Loss nan (nan)	Prec@1 1.000 (2.455)	Prec@5 14.000 (12.545)
Test: [20/39]	Time 0.542 (0.583)	Loss nan (nan)	Prec@1 1.000 (2.381)	Prec@5 12.000 (12.571)
Test: [30/39]	Time 0.593 (0.574)	Loss nan (nan)	Prec@1 3.000 (2.452)	Prec@5 12.000 (12.290)
Test: [30/39]	Time 0.593 (0.574)	Loss nan (nan)	Prec@1 3.000 (2.452)	Prec@5 12.000 (12.290)

=============================
Finished training epoch: 4/50
@ 2018-12-20 18:24:36.562591
=============================

============================
Start training epoch: 5/50
@ 2018-12-20 18:24:36.563099
============================
Epoch: [5][0/354]	Time 2.586 (2.586)	Data 0.134 (0.134)	Loss nan (nan)	Prec@1 1.000 (1.000)	Prec@5 9.000 (9.000)
Epoch: [5][10/354]	Time 1.979 (2.061)	Data 0.004 (0.014)	Loss nan (nan)	Prec@1 0.000 (1.364)	Prec@5 16.000 (14.636)
Epoch: [5][20/354]	Time 2.033 (2.033)	Data 0.002 (0.009)	Loss nan (nan)	Prec@1 0.000 (1.048)	Prec@5 15.000 (14.476)
Epoch: [5][30/354]	Time 2.003 (2.022)	Data 0.002 (0.007)	Loss nan (nan)	Prec@1 0.000 (0.871)	Prec@5 14.000 (15.065)
Epoch: [5][40/354]	Time 1.954 (2.018)	Data 0.003 (0.005)	Loss nan (nan)	Prec@1 0.000 (0.878)	Prec@5 10.000 (14.390)
Epoch: [5][50/354]	Time 1.995 (2.014)	Data 0.002 (0.005)	Loss nan (nan)	Prec@1 1.000 (0.784)	Prec@5 19.000 (14.922)
Epoch: [5][60/354]	Time 1.967 (2.010)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.770)	Prec@5 12.000 (14.721)
Epoch: [5][70/354]	Time 1.979 (2.014)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.746)	Prec@5 11.000 (14.352)
Epoch: [5][80/354]	Time 2.015 (2.014)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.728)	Prec@5 10.000 (14.333)
Epoch: [5][90/354]	Time 2.010 (2.013)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.780)	Prec@5 9.000 (14.319)
Epoch: [5][100/354]	Time 2.007 (2.014)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.802)	Prec@5 17.000 (14.535)
Epoch: [5][110/354]	Time 1.980 (2.012)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 3.000 (0.856)	Prec@5 14.000 (14.468)
Epoch: [5][120/354]	Time 2.040 (2.012)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.860)	Prec@5 19.000 (14.463)
Epoch: [5][130/354]	Time 1.964 (2.012)	Data 0.003 (0.004)	Loss nan (nan)	Prec@1 0.000 (0.855)	Prec@5 9.000 (14.504)
Epoch: [5][140/354]	Time 1.991 (2.010)	Data 0.002 (0.004)	Loss nan (nan)	Prec@1 1.000 (0.823)	Prec@5 16.000 (14.482)
Epoch: [5][150/354]	Time 2.014 (2.009)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.834)	Prec@5 14.000 (14.510)
Epoch: [5][160/354]	Time 1.952 (2.010)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 2.000 (0.870)	Prec@5 15.000 (14.534)
Epoch: [5][170/354]	Time 2.094 (2.009)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 3.000 (0.936)	Prec@5 15.000 (14.579)
Epoch: [5][180/354]	Time 1.996 (2.009)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.939)	Prec@5 15.000 (14.652)
Epoch: [5][190/354]	Time 1.924 (2.008)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.937)	Prec@5 13.000 (14.602)
Epoch: [5][200/354]	Time 1.995 (2.009)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 3.000 (0.960)	Prec@5 19.000 (14.667)
Epoch: [5][210/354]	Time 2.065 (2.009)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.943)	Prec@5 16.000 (14.673)
Epoch: [5][220/354]	Time 2.039 (2.010)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.928)	Prec@5 14.000 (14.701)
Epoch: [5][230/354]	Time 2.028 (2.009)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.935)	Prec@5 19.000 (14.792)
Epoch: [5][240/354]	Time 2.028 (2.008)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.938)	Prec@5 15.000 (14.801)
Epoch: [5][250/354]	Time 1.993 (2.008)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.948)	Prec@5 16.000 (14.809)
Epoch: [5][260/354]	Time 1.960 (2.008)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 3.000 (0.969)	Prec@5 14.000 (14.774)
Epoch: [5][270/354]	Time 2.016 (2.008)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 2.000 (0.963)	Prec@5 17.000 (14.812)
Epoch: [5][280/354]	Time 2.069 (2.008)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.979)	Prec@5 16.000 (14.808)
Epoch: [5][290/354]	Time 2.042 (2.007)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.973)	Prec@5 16.000 (14.845)
Epoch: [5][300/354]	Time 2.130 (2.007)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.970)	Prec@5 13.000 (14.857)
Epoch: [5][310/354]	Time 2.028 (2.007)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.961)	Prec@5 13.000 (14.894)
Epoch: [5][320/354]	Time 2.006 (2.007)	Data 0.003 (0.003)	Loss nan (nan)	Prec@1 0.000 (0.960)	Prec@5 12.000 (14.919)
Epoch: [5][330/354]	Time 2.034 (2.008)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.940)	Prec@5 21.000 (14.909)
Epoch: [5][340/354]	Time 2.006 (2.008)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.947)	Prec@5 18.000 (14.947)
Epoch: [5][350/354]	Time 1.985 (2.007)	Data 0.002 (0.003)	Loss nan (nan)	Prec@1 1.000 (0.940)	Prec@5 15.000 (14.869)
Test: [0/39]	Time 0.824 (0.824)	Loss nan (nan)	Prec@1 2.000 (2.000)	Prec@5 6.000 (6.000)
Test: [10/39]	Time 0.546 (0.584)	Loss nan (nan)	Prec@1 2.000 (1.909)	Prec@5 9.000 (11.000)
Test: [20/39]	Time 0.546 (0.570)	Loss nan (nan)	Prec@1 1.000 (1.952)	Prec@5 10.000 (11.190)
Test: [30/39]	Time 0.609 (0.572)	Loss nan (nan)	Prec@1 0.000 (2.258)	Prec@5 10.000 (11.677)
Test: [30/39]	Time 0.609 (0.572)	Loss nan (nan)	Prec@1 0.000 (2.258)	Prec@5 10.000 (11.677)

=============================
Finished training epoch: 5/50
@ 2018-12-20 18:36:48.077169
=============================
--------------------------------------------------------------------------------------------------------------