Epoch 1
-------------------------------
loss: 2.166446  [   64/60000]
loss: 2.166381  [ 6464/60000]
loss: 2.166781  [12864/60000]
loss: 2.166728  [19264/60000]
loss: 2.166584  [25664/60000]
loss: 2.165938  [32064/60000]
loss: 2.166313  [38464/60000]
loss: 2.182053  [44864/60000]
loss: 2.168076  [51264/60000]
loss: 2.167552  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.9%, Avg loss: 2.165903 

Epoch 2
-------------------------------
loss: 2.166419  [   64/60000]
loss: 2.166301  [ 6464/60000]
loss: 2.166786  [12864/60000]
loss: 2.166711  [19264/60000]
loss: 2.166571  [25664/60000]
loss: 2.165939  [32064/60000]
loss: 2.166308  [38464/60000]
loss: 2.182044  [44864/60000]
loss: 2.168067  [51264/60000]
loss: 2.167536  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.0%, Avg loss: 2.165887 

Epoch 3
-------------------------------
loss: 2.166396  [   64/60000]
loss: 2.166274  [ 6464/60000]
loss: 2.166779  [12864/60000]
loss: 2.166693  [19264/60000]
loss: 2.166557  [25664/60000]
loss: 2.165933  [32064/60000]
loss: 2.166298  [38464/60000]
loss: 2.182032  [44864/60000]
loss: 2.168061  [51264/60000]
loss: 2.167517  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.0%, Avg loss: 2.165871 

Epoch 4
-------------------------------
loss: 2.166376  [   64/60000]
loss: 2.166254  [ 6464/60000]
loss: 2.166772  [12864/60000]
loss: 2.166678  [19264/60000]
loss: 2.166543  [25664/60000]
loss: 2.165924  [32064/60000]
loss: 2.166284  [38464/60000]
loss: 2.182019  [44864/60000]
loss: 2.168056  [51264/60000]
loss: 2.167498  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.0%, Avg loss: 2.165856 

Epoch 5
-------------------------------
loss: 2.166358  [   64/60000]
loss: 2.166234  [ 6464/60000]
loss: 2.166767  [12864/60000]
loss: 2.166664  [19264/60000]
loss: 2.166530  [25664/60000]
loss: 2.165913  [32064/60000]
loss: 2.166270  [38464/60000]
loss: 2.182007  [44864/60000]
loss: 2.168051  [51264/60000]
loss: 2.167480  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.0%, Avg loss: 2.165842 

Epoch 6
-------------------------------
loss: 2.166341  [   64/60000]
loss: 2.166216  [ 6464/60000]
loss: 2.166764  [12864/60000]
loss: 2.166650  [19264/60000]
loss: 2.166517  [25664/60000]
loss: 2.165901  [32064/60000]
loss: 2.166254  [38464/60000]
loss: 2.181995  [44864/60000]
loss: 2.168047  [51264/60000]
loss: 2.167463  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.1%, Avg loss: 2.165827 

Epoch 7
-------------------------------
loss: 2.166324  [   64/60000]
loss: 2.166198  [ 6464/60000]
loss: 2.166761  [12864/60000]
loss: 2.166636  [19264/60000]
loss: 2.166504  [25664/60000]
loss: 2.165890  [32064/60000]
loss: 2.166238  [38464/60000]
loss: 2.181984  [44864/60000]
loss: 2.168043  [51264/60000]
loss: 2.167447  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.1%, Avg loss: 2.165813 

Epoch 8
-------------------------------
loss: 2.166309  [   64/60000]
loss: 2.166181  [ 6464/60000]
loss: 2.166759  [12864/60000]
loss: 2.166623  [19264/60000]
loss: 2.166492  [25664/60000]
loss: 2.165878  [32064/60000]
loss: 2.166222  [38464/60000]
loss: 2.181973  [44864/60000]
loss: 2.168040  [51264/60000]
loss: 2.167432  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.1%, Avg loss: 2.165800 

Epoch 9
-------------------------------
loss: 2.166296  [   64/60000]
loss: 2.166165  [ 6464/60000]
loss: 2.166756  [12864/60000]
loss: 2.166609  [19264/60000]
loss: 2.166480  [25664/60000]
loss: 2.165867  [32064/60000]
loss: 2.166206  [38464/60000]
loss: 2.181962  [44864/60000]
loss: 2.168035  [51264/60000]
loss: 2.167418  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.1%, Avg loss: 2.165787 

Epoch 10
-------------------------------
loss: 2.166283  [   64/60000]
loss: 2.166149  [ 6464/60000]
loss: 2.166749  [12864/60000]
loss: 2.166595  [19264/60000]
loss: 2.166467  [25664/60000]
loss: 2.165856  [32064/60000]
loss: 2.166191  [38464/60000]
loss: 2.181952  [44864/60000]
loss: 2.168031  [51264/60000]
loss: 2.167405  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.1%, Avg loss: 2.165775 

Epoch 11
-------------------------------
loss: 2.166271  [   64/60000]
loss: 2.166136  [ 6464/60000]
loss: 2.166739  [12864/60000]
loss: 2.166581  [19264/60000]
loss: 2.166455  [25664/60000]
loss: 2.165845  [32064/60000]
loss: 2.166175  [38464/60000]
loss: 2.181942  [44864/60000]
loss: 2.168026  [51264/60000]
loss: 2.167392  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.1%, Avg loss: 2.165763 

Epoch 12
-------------------------------
loss: 2.166260  [   64/60000]
loss: 2.166123  [ 6464/60000]
loss: 2.166726  [12864/60000]
loss: 2.166567  [19264/60000]
loss: 2.166443  [25664/60000]
loss: 2.165834  [32064/60000]
loss: 2.166161  [38464/60000]
loss: 2.181932  [44864/60000]
loss: 2.168021  [51264/60000]
loss: 2.167379  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.2%, Avg loss: 2.165751 

Epoch 13
-------------------------------
loss: 2.166249  [   64/60000]
loss: 2.166112  [ 6464/60000]
loss: 2.166711  [12864/60000]
loss: 2.166553  [19264/60000]
loss: 2.166430  [25664/60000]
loss: 2.165823  [32064/60000]
loss: 2.166147  [38464/60000]
loss: 2.181923  [44864/60000]
loss: 2.168016  [51264/60000]
loss: 2.167367  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.2%, Avg loss: 2.165739 

Epoch 14
-------------------------------
loss: 2.166238  [   64/60000]
loss: 2.166101  [ 6464/60000]
loss: 2.166696  [12864/60000]
loss: 2.166539  [19264/60000]
loss: 2.166418  [25664/60000]
loss: 2.165812  [32064/60000]
loss: 2.166133  [38464/60000]
loss: 2.181914  [44864/60000]
loss: 2.168010  [51264/60000]
loss: 2.167355  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.3%, Avg loss: 2.165728 

Epoch 15
-------------------------------
loss: 2.166227  [   64/60000]
loss: 2.166093  [ 6464/60000]
loss: 2.166680  [12864/60000]
loss: 2.166526  [19264/60000]
loss: 2.166406  [25664/60000]
loss: 2.165802  [32064/60000]
loss: 2.166120  [38464/60000]
loss: 2.181906  [44864/60000]
loss: 2.168005  [51264/60000]
loss: 2.167342  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.3%, Avg loss: 2.165717 

Epoch 16
-------------------------------
loss: 2.166217  [   64/60000]
loss: 2.166085  [ 6464/60000]
loss: 2.166664  [12864/60000]
loss: 2.166512  [19264/60000]
loss: 2.166394  [25664/60000]
loss: 2.165791  [32064/60000]
loss: 2.166106  [38464/60000]
loss: 2.181897  [44864/60000]
loss: 2.167998  [51264/60000]
loss: 2.167329  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.3%, Avg loss: 2.165706 

Epoch 17
-------------------------------
loss: 2.166205  [   64/60000]
loss: 2.166078  [ 6464/60000]
loss: 2.166648  [12864/60000]
loss: 2.166500  [19264/60000]
loss: 2.166380  [25664/60000]
loss: 2.165781  [32064/60000]
loss: 2.166093  [38464/60000]
loss: 2.181890  [44864/60000]
loss: 2.167992  [51264/60000]
loss: 2.167316  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.4%, Avg loss: 2.165695 

Epoch 18
-------------------------------
loss: 2.166194  [   64/60000]
loss: 2.166073  [ 6464/60000]
loss: 2.166631  [12864/60000]
loss: 2.166488  [19264/60000]
loss: 2.166368  [25664/60000]
loss: 2.165772  [32064/60000]
loss: 2.166080  [38464/60000]
loss: 2.181882  [44864/60000]
loss: 2.167986  [51264/60000]
loss: 2.167304  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.4%, Avg loss: 2.165685 

Epoch 19
-------------------------------
loss: 2.166183  [   64/60000]
loss: 2.166068  [ 6464/60000]
loss: 2.166615  [12864/60000]
loss: 2.166476  [19264/60000]
loss: 2.166354  [25664/60000]
loss: 2.165763  [32064/60000]
loss: 2.166066  [38464/60000]
loss: 2.181874  [44864/60000]
loss: 2.167980  [51264/60000]
loss: 2.167291  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.4%, Avg loss: 2.165675 

Epoch 20
-------------------------------
loss: 2.166173  [   64/60000]
loss: 2.166065  [ 6464/60000]
loss: 2.166599  [12864/60000]
loss: 2.166465  [19264/60000]
loss: 2.166340  [25664/60000]
loss: 2.165754  [32064/60000]
loss: 2.166052  [38464/60000]
loss: 2.181867  [44864/60000]
loss: 2.167974  [51264/60000]
loss: 2.167278  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.4%, Avg loss: 2.165665 

Epoch 21
-------------------------------
loss: 2.166161  [   64/60000]
loss: 2.166062  [ 6464/60000]
loss: 2.166583  [12864/60000]
loss: 2.166454  [19264/60000]
loss: 2.166326  [25664/60000]
loss: 2.165745  [32064/60000]
loss: 2.166039  [38464/60000]
loss: 2.181860  [44864/60000]
loss: 2.167968  [51264/60000]
loss: 2.167266  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.4%, Avg loss: 2.165655 

Epoch 22
-------------------------------
loss: 2.166150  [   64/60000]
loss: 2.166061  [ 6464/60000]
loss: 2.166567  [12864/60000]
loss: 2.166444  [19264/60000]
loss: 2.166312  [25664/60000]
loss: 2.165738  [32064/60000]
loss: 2.166025  [38464/60000]
loss: 2.181852  [44864/60000]
loss: 2.167963  [51264/60000]
loss: 2.167254  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.4%, Avg loss: 2.165645 

Epoch 23
-------------------------------
loss: 2.166139  [   64/60000]
loss: 2.166061  [ 6464/60000]
loss: 2.166551  [12864/60000]
loss: 2.166433  [19264/60000]
loss: 2.166297  [25664/60000]
loss: 2.165730  [32064/60000]
loss: 2.166011  [38464/60000]
loss: 2.181844  [44864/60000]
loss: 2.167958  [51264/60000]
loss: 2.167243  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.4%, Avg loss: 2.165636 

Epoch 24
-------------------------------
loss: 2.166127  [   64/60000]
loss: 2.166061  [ 6464/60000]
loss: 2.166535  [12864/60000]
loss: 2.166423  [19264/60000]
loss: 2.166281  [25664/60000]
loss: 2.165724  [32064/60000]
loss: 2.165997  [38464/60000]
loss: 2.181836  [44864/60000]
loss: 2.167954  [51264/60000]
loss: 2.167232  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.5%, Avg loss: 2.165626 

Epoch 25
-------------------------------
loss: 2.166116  [   64/60000]
loss: 2.166064  [ 6464/60000]
loss: 2.166519  [12864/60000]
loss: 2.166414  [19264/60000]
loss: 2.166266  [25664/60000]
loss: 2.165717  [32064/60000]
loss: 2.165982  [38464/60000]
loss: 2.181828  [44864/60000]
loss: 2.167950  [51264/60000]
loss: 2.167221  [57664/60000]
Entrené
Test Error: 
 Accuracy: 88.5%, Avg loss: 2.165617 


