Confused in the train of classifer.

[siam7tong]

I am so sorry to bother you again.
I have trained the snn_autonet with the delta_weight_mean : 2.05338878e-07,then i trained classifer.I got the 23.5% accuracy at first batch of 2000 examples, but i saw your result is 59.38%. Then i train the classifer with lr in your raw set ,but the last result is always around 75%~79%. I don't know what happened. I also want to use the snn_auto model in your files, but i can't load it successely. I am new in DL, there may be something i didn't notice. I really need your help, could you give me some advices. Thank you so much.

Here is the classifer trained results:

2017-12-29 09:50:42.822324: Learning rates LR: 1.000000 2017-12-29 09:52:42.108979: Iter: 2000 [0], loss: 99177.390625, acc: 0.00%, avg_loss: 85936.811722, avg_acc: 28.20% 2017-12-29 09:54:41.830846: Iter: 4000 [0], loss: 20514.828125, acc: 0.00%, avg_loss: 81864.085177, avg_acc: 34.17% 2017-12-29 09:56:40.925028: Iter: 6000 [0], loss: 0.000000, acc: 1.00%, avg_loss: 81793.218836, avg_acc: 37.23% 2017-12-29 09:58:42.632386: Iter: 8000 [0], loss: 0.000000, acc: 1.00%, avg_loss: 79121.567086, avg_acc: 40.06% 2017-12-29 10:00:41.175695: Iter: 10000 [0], loss: 42995.710938, acc: 0.00%, avg_loss: 78534.694568, avg_acc: 41.80% 2017-12-29 10:02:39.190052: Iter: 12000 [0], loss: 0.000000, acc: 1.00%, avg_loss: 77897.013161, avg_acc: 43.37% 2017-12-29 10:02:39.190443: Epoch 0: avg_Loss: 77903.505120136964, avg_Acc: 43.370280856738 2017-12-29 10:02:39.190583: Learning rates changed LR: 0.100000 2017-12-29 10:04:42.617513: Iter: 14000 [1], loss: 18558.640625, acc: 0.00%, avg_loss: 26327.748162, avg_acc: 69.40% 2017-12-29 10:06:40.323889: Iter: 16000 [1], loss: 0.000000, acc: 1.00%, avg_loss: 22216.912610, avg_acc: 71.53% 2017-12-29 10:08:40.542183: Iter: 18000 [1], loss: 0.000000, acc: 1.00%, avg_loss: 21290.121762, avg_acc: 72.17% 2017-12-29 10:10:39.691736: Iter: 20000 [1], loss: 0.000000, acc: 1.00%, avg_loss: 20656.107550, avg_acc: 72.12% 2017-12-29 10:12:36.612161: Iter: 22000 [1], loss: 0.000000, acc: 1.00%, avg_loss: 20444.464095, avg_acc: 72.05% 2017-12-29 10:14:35.092268: Iter: 24000 [1], loss: 118659.570312, acc: 0.00%, avg_loss: 20122.769527, avg_acc: 72.12% 2017-12-29 10:14:35.092541: Epoch 1: avg_Loss: 20124.446563906367, avg_Acc: 72.122676889741 2017-12-29 10:14:35.092582: Learning rates changed LR: 0.100000 2017-12-29 10:16:36.363787: Iter: 26000 [2], loss: 0.000000, acc: 1.00%, avg_loss: 17975.989564, avg_acc: 73.10% 2017-12-29 10:18:35.850549: Iter: 28000 [2], loss: 44473.710938, acc: 0.00%, avg_loss: 17481.193654, avg_acc: 73.25% 2017-12-29 10:20:34.975065: Iter: 30000 [2], loss: 0.000000, acc: 1.00%, avg_loss: 17638.474654, avg_acc: 73.12% 2017-12-29 10:22:36.304807: Iter: 32000 [2], loss: 0.000000, acc: 1.00%, avg_loss: 17455.829458, avg_acc: 72.91% 2017-12-29 10:24:34.476960: Iter: 34000 [2], loss: 0.000000, acc: 1.00%, avg_loss: 17744.024149, avg_acc: 72.76% 2017-12-29 10:26:32.921364: Iter: 36000 [2], loss: 0.000000, acc: 1.00%, avg_loss: 17528.401285, avg_acc: 72.91% 2017-12-29 10:26:32.921725: Epoch 2: avg_Loss: 17529.862106998906, avg_Acc: 72.914409534128 2017-12-29 10:26:32.921873: Learning rates changed LR: 0.010000 2017-12-29 10:28:39.264652: Iter: 38000 [3], loss: 0.000000, acc: 1.00%, avg_loss: 13054.521175, avg_acc: 75.95% 2017-12-29 10:30:26.309336: Iter: 40000 [3], loss: 0.000000, acc: 1.00%, avg_loss: 13008.920330, avg_acc: 76.38% 2017-12-29 10:32:11.410454: Iter: 42000 [3], loss: 0.000000, acc: 1.00%, avg_loss: 13880.511879, avg_acc: 76.25% 2017-12-29 10:34:05.807313: Iter: 44000 [3], loss: 23595.546875, acc: 0.00%, avg_loss: 13951.334126, avg_acc: 76.38% 2017-12-29 10:35:59.726245: Iter: 46000 [3], loss: 115823.546875, acc: 0.00%, avg_loss: 13987.278281, avg_acc: 76.33% 2017-12-29 10:37:50.667808: Iter: 48000 [3], loss: 0.000000, acc: 1.00%, avg_loss: 13714.742202, avg_acc: 76.51% 2017-12-29 10:37:50.668205: Epoch 3: avg_Loss: 13715.885192744245, avg_Acc: 76.514709559130 2017-12-29 10:37:50.668328: Learning rates changed LR: 0.001000 2017-12-29 10:39:45.801393: Iter: 50000 [4], loss: 21998.937500, acc: 0.00%, avg_loss: 14667.064186, avg_acc: 76.35% 2017-12-29 10:41:26.169617: Iter: 52000 [4], loss: 0.000000, acc: 1.00%, avg_loss: 13953.024168, avg_acc: 76.68% 2017-12-29 10:43:24.667357: Iter: 54000 [4], loss: 0.000000, acc: 1.00%, avg_loss: 13596.672170, avg_acc: 76.80% 2017-12-29 10:45:24.305483: Iter: 56000 [4], loss: 0.000000, acc: 1.00%, avg_loss: 13453.069836, avg_acc: 76.76% 2017-12-29 10:47:23.165318: Iter: 58000 [4], loss: 0.000000, acc: 1.00%, avg_loss: 13597.539448, avg_acc: 76.82% 2017-12-29 10:49:21.349746: Iter: 60000 [4], loss:0.000000, acc: 1.00%, avg_loss: 19883.241314, avg_acc: 76.33% 2017-12-20 10:37:11.733672: Epoch 4: avg_Loss: 19884.898389319776, avg_Acc: 76.331527627302 2017-12-20 10:37:11.733709: Learning rates changed LR: 0.001000 2017-12-20 10:38:41.183413: Iter: 62000 [5], loss: 0.000000, acc: 1.00%, avg_loss: 18197.470076, avg_acc: 79.40% 2017-12-20 10:40:07.714596: Iter: 64000 [5], loss: 0.000000, acc: 1.00%, avg_loss: 18597.421187, avg_acc: 78.80% 2017-12-20 10:41:39.236288: Iter: 66000 [5], loss: 87509.156250, acc: 0.00%, avg_loss: 19343.797669, avg_acc: 78.13% 2017-12-20 10:43:02.023565: Iter: 68000 [5], loss: 2268.468750, acc: 0.00%, avg_loss: 19525.549633, avg_acc: 78.16% 2017-12-20 10:44:27.446234: Iter: 70000 [5], loss: 0.000000, acc: 1.00%, avg_loss: 19540.495293, avg_acc: 78.36% 2017-12-20 10:45:55.687838: Iter: 72000 [5], loss: 7991.203125, acc: 0.00%, avg_loss: 19458.476607, avg_acc: 78.35% 2017-12-20 10:45:55.688114: Epoch 5: avg_Loss: 19460.098281627750, avg_Acc: 78.356529710809