Epoch 1
-------------------------------
loss: 2.308425  [   64/60000]
loss: 2.234377  [ 6464/60000]
loss: 2.210187  [12864/60000]
loss: 2.174686  [19264/60000]
loss: 2.183035  [25664/60000]
loss: 2.176858  [32064/60000]
loss: 2.169878  [38464/60000]
loss: 2.183950  [44864/60000]
loss: 2.168637  [51264/60000]
loss: 2.174931  [57664/60000]
Entrené
Test Error: 
 Accuracy: 78.8%, Avg loss: 2.169967 

Epoch 2
-------------------------------
loss: 2.167745  [   64/60000]
loss: 2.169796  [ 6464/60000]
loss: 2.182372  [12864/60000]
loss: 2.168083  [19264/60000]
loss: 2.171852  [25664/60000]
loss: 2.171926  [32064/60000]
loss: 2.167753  [38464/60000]
loss: 2.183874  [44864/60000]
loss: 2.168081  [51264/60000]
loss: 2.170562  [57664/60000]
Entrené
Test Error: 
 Accuracy: 81.5%, Avg loss: 2.168588 

Epoch 3
-------------------------------
loss: 2.167156  [   64/60000]
loss: 2.168966  [ 6464/60000]
loss: 2.181912  [12864/60000]
loss: 2.168084  [19264/60000]
loss: 2.168857  [25664/60000]
loss: 2.169609  [32064/60000]
loss: 2.167145  [38464/60000]
loss: 2.183901  [44864/60000]
loss: 2.168076  [51264/60000]
loss: 2.169792  [57664/60000]
Entrené
Test Error: 
 Accuracy: 82.9%, Avg loss: 2.168122 

Epoch 4
-------------------------------
loss: 2.167106  [   64/60000]
loss: 2.168642  [ 6464/60000]
loss: 2.181221  [12864/60000]
loss: 2.168004  [19264/60000]
loss: 2.168434  [25664/60000]
loss: 2.168736  [32064/60000]
loss: 2.166957  [38464/60000]
loss: 2.183827  [44864/60000]
loss: 2.168139  [51264/60000]
loss: 2.169409  [57664/60000]
Entrené
Test Error: 
 Accuracy: 83.7%, Avg loss: 2.167817 

Epoch 5
-------------------------------
loss: 2.167343  [   64/60000]
loss: 2.168457  [ 6464/60000]
loss: 2.180758  [12864/60000]
loss: 2.167912  [19264/60000]
loss: 2.168210  [25664/60000]
loss: 2.168226  [32064/60000]
loss: 2.166982  [38464/60000]
loss: 2.183723  [44864/60000]
loss: 2.168197  [51264/60000]
loss: 2.169136  [57664/60000]
Entrené
Test Error: 
 Accuracy: 84.3%, Avg loss: 2.167589 

Epoch 6
-------------------------------
loss: 2.167565  [   64/60000]
loss: 2.168301  [ 6464/60000]
loss: 2.180338  [12864/60000]
loss: 2.167842  [19264/60000]
loss: 2.168005  [25664/60000]
loss: 2.167830  [32064/60000]
loss: 2.167014  [38464/60000]
loss: 2.183610  [44864/60000]
loss: 2.168263  [51264/60000]
loss: 2.168949  [57664/60000]
Entrené
Test Error: 
 Accuracy: 84.7%, Avg loss: 2.167392 

Epoch 7
-------------------------------
loss: 2.167648  [   64/60000]
loss: 2.168119  [ 6464/60000]
loss: 2.179776  [12864/60000]
loss: 2.167804  [19264/60000]
loss: 2.167878  [25664/60000]
loss: 2.167571  [32064/60000]
loss: 2.166999  [38464/60000]
loss: 2.183482  [44864/60000]
loss: 2.168299  [51264/60000]
loss: 2.168837  [57664/60000]
Entrené
Test Error: 
 Accuracy: 85.0%, Avg loss: 2.167222 

Epoch 8
-------------------------------
loss: 2.167617  [   64/60000]
loss: 2.167904  [ 6464/60000]
loss: 2.178975  [12864/60000]
loss: 2.167773  [19264/60000]
loss: 2.167767  [25664/60000]
loss: 2.167401  [32064/60000]
loss: 2.166958  [38464/60000]
loss: 2.183343  [44864/60000]
loss: 2.168296  [51264/60000]
loss: 2.168738  [57664/60000]
Entrené
Test Error: 
 Accuracy: 85.3%, Avg loss: 2.167076 

Epoch 9
-------------------------------
loss: 2.167533  [   64/60000]
loss: 2.167657  [ 6464/60000]
loss: 2.177711  [12864/60000]
loss: 2.167751  [19264/60000]
loss: 2.167663  [25664/60000]
loss: 2.167248  [32064/60000]
loss: 2.166923  [38464/60000]
loss: 2.183187  [44864/60000]
loss: 2.168261  [51264/60000]
loss: 2.168640  [57664/60000]
Entrené
Test Error: 
 Accuracy: 85.6%, Avg loss: 2.166948 

Epoch 10
-------------------------------
loss: 2.167426  [   64/60000]
loss: 2.167419  [ 6464/60000]
loss: 2.175740  [12864/60000]
loss: 2.167720  [19264/60000]
loss: 2.167569  [25664/60000]
loss: 2.167110  [32064/60000]
loss: 2.166903  [38464/60000]
loss: 2.183005  [44864/60000]
loss: 2.168214  [51264/60000]
loss: 2.168542  [57664/60000]
Entrené
Test Error: 
 Accuracy: 85.9%, Avg loss: 2.166840 

Epoch 11
-------------------------------
loss: 2.167327  [   64/60000]
loss: 2.167226  [ 6464/60000]
loss: 2.173174  [12864/60000]
loss: 2.167662  [19264/60000]
loss: 2.167473  [25664/60000]
loss: 2.166973  [32064/60000]
loss: 2.166882  [38464/60000]
loss: 2.182842  [44864/60000]
loss: 2.168169  [51264/60000]
loss: 2.168446  [57664/60000]
Entrené
Test Error: 
 Accuracy: 86.2%, Avg loss: 2.166746 

Epoch 12
-------------------------------
loss: 2.167241  [   64/60000]
loss: 2.167085  [ 6464/60000]
loss: 2.171004  [12864/60000]
loss: 2.167586  [19264/60000]
loss: 2.167371  [25664/60000]
loss: 2.166838  [32064/60000]
loss: 2.166847  [38464/60000]
loss: 2.182729  [44864/60000]
loss: 2.168133  [51264/60000]
loss: 2.168359  [57664/60000]
Entrené
Test Error: 
 Accuracy: 86.3%, Avg loss: 2.166656 

Epoch 13
-------------------------------
loss: 2.167165  [   64/60000]
loss: 2.166980  [ 6464/60000]
loss: 2.169821  [12864/60000]
loss: 2.167500  [19264/60000]
loss: 2.167265  [25664/60000]
loss: 2.166734  [32064/60000]
loss: 2.166811  [38464/60000]
loss: 2.182642  [44864/60000]
loss: 2.168114  [51264/60000]
loss: 2.168282  [57664/60000]
Entrené
Test Error: 
 Accuracy: 86.5%, Avg loss: 2.166571 

Epoch 14
-------------------------------
loss: 2.167089  [   64/60000]
loss: 2.166896  [ 6464/60000]
loss: 2.169113  [12864/60000]
loss: 2.167411  [19264/60000]
loss: 2.167170  [25664/60000]
loss: 2.166651  [32064/60000]
loss: 2.166772  [38464/60000]
loss: 2.182571  [44864/60000]
loss: 2.168108  [51264/60000]
loss: 2.168212  [57664/60000]
Entrené
Test Error: 
 Accuracy: 86.6%, Avg loss: 2.166492 

Epoch 15
-------------------------------
loss: 2.167016  [   64/60000]
loss: 2.166828  [ 6464/60000]
loss: 2.168597  [12864/60000]
loss: 2.167322  [19264/60000]
loss: 2.167085  [25664/60000]
loss: 2.166579  [32064/60000]
loss: 2.166732  [38464/60000]
loss: 2.182515  [44864/60000]
loss: 2.168111  [51264/60000]
loss: 2.168150  [57664/60000]
Entrené
Test Error: 
 Accuracy: 86.9%, Avg loss: 2.166418 

Epoch 16
-------------------------------
loss: 2.166947  [   64/60000]
loss: 2.166772  [ 6464/60000]
loss: 2.168248  [12864/60000]
loss: 2.167235  [19264/60000]
loss: 2.167011  [25664/60000]
loss: 2.166515  [32064/60000]
loss: 2.166692  [38464/60000]
loss: 2.182470  [44864/60000]
loss: 2.168117  [51264/60000]
loss: 2.168092  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.2%, Avg loss: 2.166351 

Epoch 17
-------------------------------
loss: 2.166883  [   64/60000]
loss: 2.166725  [ 6464/60000]
loss: 2.167996  [12864/60000]
loss: 2.167153  [19264/60000]
loss: 2.166946  [25664/60000]
loss: 2.166455  [32064/60000]
loss: 2.166654  [38464/60000]
loss: 2.182435  [44864/60000]
loss: 2.168126  [51264/60000]
loss: 2.168032  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.3%, Avg loss: 2.166288 

Epoch 18
-------------------------------
loss: 2.166822  [   64/60000]
loss: 2.166683  [ 6464/60000]
loss: 2.167782  [12864/60000]
loss: 2.167078  [19264/60000]
loss: 2.166888  [25664/60000]
loss: 2.166398  [32064/60000]
loss: 2.166616  [38464/60000]
loss: 2.182408  [44864/60000]
loss: 2.168136  [51264/60000]
loss: 2.167969  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.3%, Avg loss: 2.166230 

Epoch 19
-------------------------------
loss: 2.166760  [   64/60000]
loss: 2.166642  [ 6464/60000]
loss: 2.167600  [12864/60000]
loss: 2.167010  [19264/60000]
loss: 2.166835  [25664/60000]
loss: 2.166342  [32064/60000]
loss: 2.166579  [38464/60000]
loss: 2.182388  [44864/60000]
loss: 2.168147  [51264/60000]
loss: 2.167903  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.5%, Avg loss: 2.166176 

Epoch 20
-------------------------------
loss: 2.166698  [   64/60000]
loss: 2.166600  [ 6464/60000]
loss: 2.167451  [12864/60000]
loss: 2.166949  [19264/60000]
loss: 2.166785  [25664/60000]
loss: 2.166289  [32064/60000]
loss: 2.166541  [38464/60000]
loss: 2.182371  [44864/60000]
loss: 2.168159  [51264/60000]
loss: 2.167839  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.5%, Avg loss: 2.166126 

Epoch 21
-------------------------------
loss: 2.166633  [   64/60000]
loss: 2.166556  [ 6464/60000]
loss: 2.167332  [12864/60000]
loss: 2.166894  [19264/60000]
loss: 2.166739  [25664/60000]
loss: 2.166239  [32064/60000]
loss: 2.166502  [38464/60000]
loss: 2.182349  [44864/60000]
loss: 2.168168  [51264/60000]
loss: 2.167777  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.6%, Avg loss: 2.166079 

Epoch 22
-------------------------------
loss: 2.166572  [   64/60000]
loss: 2.166511  [ 6464/60000]
loss: 2.167234  [12864/60000]
loss: 2.166848  [19264/60000]
loss: 2.166693  [25664/60000]
loss: 2.166190  [32064/60000]
loss: 2.166462  [38464/60000]
loss: 2.182311  [44864/60000]
loss: 2.168173  [51264/60000]
loss: 2.167724  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.6%, Avg loss: 2.166034 

Epoch 23
-------------------------------
loss: 2.166529  [   64/60000]
loss: 2.166467  [ 6464/60000]
loss: 2.167150  [12864/60000]
loss: 2.166811  [19264/60000]
loss: 2.166652  [25664/60000]
loss: 2.166147  [32064/60000]
loss: 2.166422  [38464/60000]
loss: 2.182256  [44864/60000]
loss: 2.168168  [51264/60000]
loss: 2.167680  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.7%, Avg loss: 2.165992 

Epoch 24
-------------------------------
loss: 2.166505  [   64/60000]
loss: 2.166423  [ 6464/60000]
loss: 2.167077  [12864/60000]
loss: 2.166782  [19264/60000]
loss: 2.166617  [25664/60000]
loss: 2.166113  [32064/60000]
loss: 2.166383  [38464/60000]
loss: 2.182204  [44864/60000]
loss: 2.168159  [51264/60000]
loss: 2.167638  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.8%, Avg loss: 2.165955 

Epoch 25
-------------------------------
loss: 2.166476  [   64/60000]
loss: 2.166381  [ 6464/60000]
loss: 2.167016  [12864/60000]
loss: 2.166748  [19264/60000]
loss: 2.166587  [25664/60000]
loss: 2.166082  [32064/60000]
loss: 2.166348  [38464/60000]
loss: 2.182160  [44864/60000]
loss: 2.168150  [51264/60000]
loss: 2.167595  [57664/60000]
Entrené
Test Error: 
 Accuracy: 87.9%, Avg loss: 2.165922 


