can not reproduce the results

[junleiz]

![image](https://user-images.githubusercontent.com/26025961/77132458-8a898e80-6a9a-11ea-9782-4398f851b752.png
I trained the resnet32 on cifar100 with 2 grafting setting. I set the seed 1 for the first model and 2 for the second. But I found the acc even decreased.

etwork:2 epoch:0 accuracy:14.700 best:14.700
Network:2 epoch:1 accuracy:16.540 best:16.540
Network:2 epoch:2 accuracy:24.700 best:24.700
Network:2 epoch:3 accuracy:23.520 best:24.700
Network:2 epoch:4 accuracy:32.250 best:32.250
Network:2 epoch:5 accuracy:34.070 best:34.070
Network:2 epoch:6 accuracy:37.280 best:37.280
Network:2 epoch:7 accuracy:37.320 best:37.320
Network:2 epoch:8 accuracy:36.540 best:37.320
Network:2 epoch:9 accuracy:40.050 best:40.050
Network:2 epoch:10 accuracy:44.270 best:44.270
Network:2 epoch:11 accuracy:39.200 best:44.270
Network:2 epoch:12 accuracy:38.870 best:44.270
Network:2 epoch:13 accuracy:44.300 best:44.300
Network:2 epoch:14 accuracy:43.690 best:44.300
Network:2 epoch:15 accuracy:38.960 best:44.300
Network:2 epoch:16 accuracy:47.530 best:47.530
Network:2 epoch:17 accuracy:40.490 best:47.530
Network:2 epoch:18 accuracy:46.240 best:47.530
Network:2 epoch:19 accuracy:40.100 best:47.530
Network:2 epoch:20 accuracy:41.650 best:47.530
Network:2 epoch:21 accuracy:44.300 best:47.530
Network:2 epoch:22 accuracy:44.440 best:47.530
Network:2 epoch:23 accuracy:44.870 best:47.530
Network:2 epoch:24 accuracy:43.860 best:47.530
Network:2 epoch:25 accuracy:46.760 best:47.530
Network:2 epoch:26 accuracy:32.770 best:47.530
Network:2 epoch:27 accuracy:42.220 best:47.530
Network:2 epoch:28 accuracy:48.920 best:48.920
Network:2 epoch:29 accuracy:44.960 best:48.920
Network:2 epoch:30 accuracy:45.670 best:48.920
Network:2 epoch:31 accuracy:45.630 best:48.920
Network:2 epoch:32 accuracy:45.840 best:48.920
Network:2 epoch:33 accuracy:46.910 best:48.920
Network:2 epoch:34 accuracy:51.240 best:51.240
Network:2 epoch:35 accuracy:48.490 best:51.240
Network:2 epoch:36 accuracy:49.460 best:51.240
Network:2 epoch:37 accuracy:45.080 best:51.240
Network:2 epoch:38 accuracy:49.390 best:51.240
Network:2 epoch:39 accuracy:45.370 best:51.240
Network:2 epoch:40 accuracy:40.510 best:51.240
Network:2 epoch:41 accuracy:39.560 best:51.240
Network:2 epoch:42 accuracy:46.540 best:51.240
Network:2 epoch:43 accuracy:48.780 best:51.240
Network:2 epoch:44 accuracy:49.220 best:51.240
Network:2 epoch:45 accuracy:46.590 best:51.240
Network:2 epoch:46 accuracy:40.120 best:51.240
Network:2 epoch:47 accuracy:44.470 best:51.240
Network:2 epoch:48 accuracy:42.030 best:51.240
Network:2 epoch:49 accuracy:47.310 best:51.240
Network:2 epoch:50 accuracy:46.580 best:51.240
Network:2 epoch:51 accuracy:45.010 best:51.240
Network:2 epoch:52 accuracy:46.270 best:51.240
Network:2 epoch:53 accuracy:47.070 best:51.240
Network:2 epoch:54 accuracy:46.270 best:51.240
Network:2 epoch:55 accuracy:49.480 best:51.240
Network:2 epoch:56 accuracy:45.360 best:51.240
Network:2 epoch:57 accuracy:46.950 best:51.240
Network:2 epoch:58 accuracy:47.840 best:51.240
Network:2 epoch:59 accuracy:52.580 best:52.580
Network:2 epoch:60 accuracy:43.420 best:52.580
Network:2 epoch:61 accuracy:68.060 best:68.060
Network:2 epoch:62 accuracy:68.230 best:68.230
Network:2 epoch:63 accuracy:68.550 best:68.550
Network:2 epoch:64 accuracy:68.750 best:68.750
Network:2 epoch:65 accuracy:68.490 best:68.750
Network:2 epoch:66 accuracy:68.290 best:68.750
Network:2 epoch:67 accuracy:68.320 best:68.750
Network:2 epoch:68 accuracy:67.870 best:68.750
Network:2 epoch:69 accuracy:68.240 best:68.750
Network:2 epoch:70 accuracy:67.790 best:68.750
Network:2 epoch:71 accuracy:67.170 best:68.750
Network:2 epoch:72 accuracy:68.130 best:68.750
Network:2 epoch:73 accuracy:68.610 best:68.750
Network:2 epoch:74 accuracy:66.910 best:68.750
Network:2 epoch:75 accuracy:66.640 best:68.750
Network:2 epoch:76 accuracy:66.710 best:68.750
Network:2 epoch:77 accuracy:66.220 best:68.750
Network:2 epoch:78 accuracy:65.440 best:68.750
Network:2 epoch:79 accuracy:66.520 best:68.750
Network:2 epoch:80 accuracy:66.810 best:68.750
Network:2 epoch:81 accuracy:66.030 best:68.750
Network:2 epoch:82 accuracy:65.430 best:68.750
Network:2 epoch:83 accuracy:66.470 best:68.750
Network:2 epoch:84 accuracy:66.250 best:68.750
Network:2 epoch:85 accuracy:65.690 best:68.750
Network:2 epoch:86 accuracy:65.500 best:68.750
Network:2 epoch:87 accuracy:66.020 best:68.750
Network:2 epoch:88 accuracy:65.160 best:68.750
Network:2 epoch:89 accuracy:63.700 best:68.750
Network:2 epoch:90 accuracy:65.590 best:68.750
Network:2 epoch:91 accuracy:65.310 best:68.750
Network:2 epoch:92 accuracy:63.440 best:68.750
Network:2 epoch:93 accuracy:64.340 best:68.750
Network:2 epoch:94 accuracy:64.090 best:68.750
Network:2 epoch:95 accuracy:64.020 best:68.750
Network:2 epoch:96 accuracy:63.130 best:68.750
Network:2 epoch:97 accuracy:62.210 best:68.750
Network:2 epoch:98 accuracy:63.610 best:68.750
Network:2 epoch:99 accuracy:63.960 best:68.750
Network:2 epoch:100 accuracy:64.730 best:68.750
Network:2 epoch:101 accuracy:65.030 best:68.750
Network:2 epoch:102 accuracy:64.990 best:68.750
Network:2 epoch:103 accuracy:64.130 best:68.750
Network:2 epoch:104 accuracy:63.280 best:68.750
Network:2 epoch:105 accuracy:63.800 best:68.750
Network:2 epoch:106 accuracy:64.050 best:68.750
Network:2 epoch:107 accuracy:63.460 best:68.750
Network:2 epoch:108 accuracy:64.790 best:68.750
Network:2 epoch:109 accuracy:64.470 best:68.750
Network:2 epoch:110 accuracy:65.210 best:68.750
Network:2 epoch:111 accuracy:64.350 best:68.750
Network:2 epoch:112 accuracy:62.980 best:68.750
Network:2 epoch:113 accuracy:63.390 best:68.750
Network:2 epoch:114 accuracy:63.860 best:68.750
Network:2 epoch:115 accuracy:64.430 best:68.750
Network:2 epoch:116 accuracy:62.950 best:68.750
Network:2 epoch:117 accuracy:63.950 best:68.750
Network:2 epoch:118 accuracy:64.000 best:68.750
Network:2 epoch:119 accuracy:63.570 best:68.750
Network:2 epoch:120 accuracy:62.570 best:68.750
Network:2 epoch:121 accuracy:70.290 best:70.290
Network:2 epoch:122 accuracy:69.990 best:70.290
Network:2 epoch:123 accuracy:70.000 best:70.290
Network:2 epoch:124 accuracy:70.210 best:70.290
Network:2 epoch:125 accuracy:69.750 best:70.290
Network:2 epoch:126 accuracy:69.850 best:70.290
Network:2 epoch:127 accuracy:70.200 best:70.290
Network:2 epoch:128 accuracy:69.730 best:70.290
Network:2 epoch:129 accuracy:69.830 best:70.290
Network:2 epoch:130 accuracy:69.780 best:70.290
Network:2 epoch:131 accuracy:69.520 best:70.290
Network:2 epoch:132 accuracy:69.560 best:70.290
Network:2 epoch:133 accuracy:69.630 best:70.290
Network:2 epoch:134 accuracy:69.770 best:70.290
Network:2 epoch:135 accuracy:69.750 best:70.290
Network:2 epoch:136 accuracy:69.390 best:70.290
Network:2 epoch:137 accuracy:69.630 best:70.290
Network:2 epoch:138 accuracy:69.250 best:70.290
Network:2 epoch:139 accuracy:69.460 best:70.290
Network:2 epoch:140 accuracy:69.420 best:70.290
Network:2 epoch:141 accuracy:69.230 best:70.290
Network:2 epoch:142 accuracy:69.490 best:70.290
Network:2 epoch:143 accuracy:69.430 best:70.290
Network:2 epoch:144 accuracy:69.220 best:70.290
Network:2 epoch:145 accuracy:69.660 best:70.290
Network:2 epoch:146 accuracy:69.330 best:70.290
Network:2 epoch:147 accuracy:69.070 best:70.290
Network:2 epoch:148 accuracy:69.260 best:70.290
Network:2 epoch:149 accuracy:69.350 best:70.290
Network:2 epoch:150 accuracy:69.130 best:70.290
Network:2 epoch:151 accuracy:69.270 best:70.290
Network:2 epoch:152 accuracy:68.890 best:70.290
Network:2 epoch:153 accuracy:69.220 best:70.290
Network:2 epoch:154 accuracy:68.980 best:70.290
Network:2 epoch:155 accuracy:68.850 best:70.290
Network:2 epoch:156 accuracy:68.970 best:70.290
Network:2 epoch:157 accuracy:69.260 best:70.290
Network:2 epoch:158 accuracy:69.140 best:70.290
Network:2 epoch:159 accuracy:69.100 best:70.290
Network:2 epoch:160 accuracy:68.860 best:70.290
Network:2 epoch:161 accuracy:68.990 best:70.290
Network:2 epoch:162 accuracy:69.120 best:70.290
Network:2 epoch:163 accuracy:68.780 best:70.290
Network:2 epoch:164 accuracy:69.190 best:70.290
Network:2 epoch:165 accuracy:68.560 best:70.290
Network:2 epoch:166 accuracy:68.860 best:70.290
Network:2 epoch:167 accuracy:68.860 best:70.290
Network:2 epoch:168 accuracy:68.620 best:70.290
Network:2 epoch:169 accuracy:69.010 best:70.290
Network:2 epoch:170 accuracy:68.760 best:70.290
Network:2 epoch:171 accuracy:68.680 best:70.290
Network:2 epoch:172 accuracy:68.950 best:70.290
Network:2 epoch:173 accuracy:68.830 best:70.290
Network:2 epoch:174 accuracy:68.740 best:70.290
Network:2 epoch:175 accuracy:68.780 best:70.290
Network:2 epoch:176 accuracy:68.620 best:70.290
Network:2 epoch:177 accuracy:68.410 best:70.290
Network:2 epoch:178 accuracy:68.540 best:70.290
Network:2 epoch:179 accuracy:68.660 best:70.290
Network:2 epoch:180 accuracy:68.610 best:70.290
Network:2 epoch:181 accuracy:68.750 best:70.290
Network:2 epoch:182 accuracy:68.690 best:70.290
Network:2 epoch:183 accuracy:68.730 best:70.290
Network:2 epoch:184 accuracy:68.710 best:70.290
Network:2 epoch:185 accuracy:68.750 best:70.290
Network:2 epoch:186 accuracy:68.590 best:70.290
Network:2 epoch:187 accuracy:68.710 best:70.290
Network:2 epoch:188 accuracy:68.740 best:70.290
Network:2 epoch:189 accuracy:68.690 best:70.290
Network:2 epoch:190 accuracy:68.820 best:70.290
Network:2 epoch:191 accuracy:68.820 best:70.290
Network:2 epoch:192 accuracy:68.480 best:70.290
Network:2 epoch:193 accuracy:68.810 best:70.290
Network:2 epoch:194 accuracy:68.760 best:70.290
Network:2 epoch:195 accuracy:68.790 best:70.290
Network:2 epoch:196 accuracy:68.770 best:70.290
Network:2 epoch:197 accuracy:68.590 best:70.290
Network:2 epoch:198 accuracy:68.730 best:70.290
Network:2 epoch:199 accuracy:68.840 best:70.290

The final act is 68.84. The corresponding baseline in your paper is 69.82

[junleiz]

The acc for the network1 is almost the same

[fxmeng]

1.log
Thanks for your attention,
First, because the learning rate drops every 60 epochs, the final result is not necessarily the best, and the best results often appear after each decline in the learning rate. As shown in the figure, the best result is 71.05 at epoch: 126. In your results, the best result of grafting should be 70.29, which is higher than the baseline.
However, your results are still lower than ours, maybe because you modified the code, please use our original version to experiment; or the environment is different, please compare the results of baseline on your environment.

[junleiz]

Thank you for your reply. I changed two things:

1. I set the batch_size for train_loader and valid_loader the same;
2. I set the random seed. Otherwise, it is hard to compare with baseline since their seeds may be totally different

I would try the origin version.