Hi friends,
I’ve noticed something strange during training a simple lenet model on mnist fashion:
The accuracy seems to not increase even thought the loss reaches as low as 0.01xx.
ain: (Epoch 45 of 100) [0665/0860]  Time: 0.00665 (4.149)  Loss: 0.40305 (0.235)  Error: 0.88672 (0.887)      Accuracy 0.11328125                               [694/1967]
Train: (Epoch 45 of 100) [0666/0860]  Time: 0.00632 (4.155)  Loss: 0.38985 (0.235)  Error: 0.89111 (0.887)      Accuracy 0.10888671875                                      
Train: (Epoch 45 of 100) [0667/0860]  Time: 0.00583 (4.161)  Loss: 0.25918 (0.235)  Error: 0.87573 (0.887)      Accuracy 0.124267578125                                     
Train: (Epoch 45 of 100) [0668/0860]  Time: 0.00621 (4.167)  Loss: 0.14686 (0.235)  Error: 0.89038 (0.887)      Accuracy 0.109619140625                                     
Train: (Epoch 45 of 100) [0669/0860]  Time: 0.00612 (4.173)  Loss: 0.27873 (0.235)  Error: 0.87988 (0.887)      Accuracy 0.1201171875                                       
Train: (Epoch 45 of 100) [0670/0860]  Time: 0.00649 (4.180)  Loss: 0.20269 (0.235)  Error: 0.88721 (0.887)      Accuracy 0.11279296875                                      
Train: (Epoch 45 of 100) [0671/0860]  Time: 0.00661 (4.186)  Loss: 0.11891 (0.235)  Error: 0.88452 (0.887)      Accuracy 0.115478515625                                     
Train: (Epoch 45 of 100) [0672/0860]  Time: 0.00604 (4.192)  Loss: 0.18655 (0.234)  Error: 0.88379 (0.887)      Accuracy 0.1162109375                                       
Train: (Epoch 45 of 100) [0673/0860]  Time: 0.00604 (4.198)  Loss: 0.35968 (0.235)  Error: 0.87866 (0.887)      Accuracy 0.121337890625                                     
Train: (Epoch 45 of 100) [0674/0860]  Time: 0.00626 (4.205)  Loss: 0.25359 (0.235)  Error: 0.87964 (0.887)      Accuracy 0.120361328125                                     
Train: (Epoch 45 of 100) [0675/0860]  Time: 0.00626 (4.211)  Loss: 0.37408 (0.235)  Error: 0.88550 (0.887)      Accuracy 0.114501953125                                     
Train: (Epoch 45 of 100) [0676/0860]  Time: 0.00641 (4.217)  Loss: 0.22567 (0.235)  Error: 0.89307 (0.887)      Accuracy 0.10693359375                                      
Train: (Epoch 45 of 100) [0677/0860]  Time: 0.00633 (4.224)  Loss: 0.33593 (0.235)  Error: 0.89526 (0.887)      Accuracy 0.104736328125                                     
Train: (Epoch 45 of 100) [0678/0860]  Time: 0.00619 (4.230)  Loss: 0.19604 (0.235)  Error: 0.88354 (0.887)      Accuracy 0.116455078125
Train: (Epoch 45 of 100) [0679/0860]  Time: 0.00603 (4.236)  Loss: 0.14126 (0.235)  Error: 0.89355 (0.887)      Accuracy 0.1064453125
Train: (Epoch 45 of 100) [0680/0860]  Time: 0.00578 (4.242)  Loss: 0.22659 (0.235)  Error: 0.89331 (0.887)      Accuracy 0.106689453125
Train: (Epoch 45 of 100) [0681/0860]  Time: 0.00628 (4.248)  Loss: 0.21508 (0.235)  Error: 0.88818 (0.887)      Accuracy 0.11181640625
Train: (Epoch 45 of 100) [0682/0860]  Time: 0.00615 (4.254)  Loss: 0.15964 (0.235)  Error: 0.88599 (0.887)      Accuracy 0.114013671875
Train: (Epoch 45 of 100) [0683/0860]  Time: 0.00603 (4.260)  Loss: 0.27280 (0.235)  Error: 0.88965 (0.887)      Accuracy 0.1103515625
Train: (Epoch 45 of 100) [0684/0860]  Time: 0.00593 (4.266)  Loss: 0.12067 (0.235)  Error: 0.88306 (0.887)      Accuracy 0.116943359375
Train: (Epoch 45 of 100) [0685/0860]  Time: 0.00636 (4.272)  Loss: 0.19499 (0.235)  Error: 0.88525 (0.887)      Accuracy 0.11474609375
Train: (Epoch 45 of 100) [0686/0860]  Time: 0.00652 (4.279)  Loss: 0.22604 (0.235)  Error: 0.88843 (0.887)      Accuracy 0.111572265625
Train: (Epoch 45 of 100) [0687/0860]  Time: 0.00633 (4.285)  Loss: 0.38691 (0.235)  Error: 0.87354 (0.887)      Accuracy 0.12646484375
Train: (Epoch 45 of 100) [0688/0860]  Time: 0.00614 (4.291)  Loss: 0.19034 (0.235)  Error: 0.88159 (0.887)      Accuracy 0.118408203125
Train: (Epoch 45 of 100) [0689/0860]  Time: 0.00618 (4.298)  Loss: 0.17172 (0.235)  Error: 0.88574 (0.887)      Accuracy 0.1142578125