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Bananaware

classificação qualidade r3

Bananaware
Feb 6th, 2020
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  1. Motion Blur: bad
  2.  
  3. a questão é que o problema não é blur. algumas amostras utilizadas:
  4. https://imgur.com/a/Y5CGIzE
  5.  
  6. testes com Laplacian (Pech-Pacheco et al. 2000, Diatom autofocusing in brightfield microscopy: a comparative study)
  7. menores números = mais blur
  8.  
  9. ==Bad|Good==
  10. Lowest: 2.8 | 2.6
  11. Highest: 707.0 | 991.9
  12. Average: 62.4 | 59.5 (!!??)
  13.  
  14.  
  15. "The reason this method works is due to the definition of the Laplacian operator itself, which is used to measure the 2nd derivative of an image. The Laplacian highlights regions of an image containing rapid intensity changes, much like the Sobel and Scharr operators. And, just like these operators, the Laplacian is often used for edge detection. The assumption here is that if an image contains high variance then there is a wide spread of responses, both edge-like and non-edge like, representative of a normal, in-focus image. But if there is very low variance, then there is a tiny spread of responses, indicating there are very little edges in the image. As we know, the more an image is blurred, the less edges there are."
  16.  
  17. sample2 https://imgur.com/a/eUKZhpm
  18.  
  19.  
  20.  
  21.  
  22. VGG-16: mais muitas horas, 99.50%
  23.  
  24. resultados intermediários (tenho esses pesos salvos). overfit?
  25. 3722/3964 93.90%
  26. 3907/3964 98.56%
  27. 3944/3964 99.50%
  28. 3883/3964 97.96%
  29.  
  30.  
  31.  
  32. logs penúltima (99.50%):
  33.  
  34. Epoch 12/17
  35. 16/3567 [..............................] - ETA: 18:27 - loss: 0.0180 - accuracy: 1.000 32/3567 [..............................] - ETA: 18:20 - loss: 0.0168 - accuracy: 1.000 48/3567 [..............................] - ETA: 18:13 - loss: 0.0168 - accuracy: 1.000 64/3567 [..............................] - ETA: 18:08 - loss: 0.0166 - accuracy: 1.000 80/3567 [..............................] - ETA: 18:04 - loss: 0.0172 - accuracy: 1.000 96/3567 [..............................] - ETA: 17:58 - loss: 0.0169 - accuracy: 1.000 112/3567 [..............................] - ETA: 17:53 - loss: 0.0196 - accuracy: 1.000 128/3567 [>.............................] - ETA: 17:48 - loss: 0.0191 - accuracy: 1.000 144/3567 [>.............................] - ETA: 17:42 - loss: 0.0200 - accuracy: 1.000 160/3567 [>.............................] - ETA: 17:38 - loss: 0.0198 - accuracy: 1.000 176/3567 [>.............................] - ETA: 17:33 - loss: 0.0204 - accuracy: 1.000 192/3567 [>.............................] - ETA: 17:28 - loss: 0.0244 - accuracy: 0.994 208/3567 [>.............................] - ETA: 17:23 - loss: 0.0238 - accuracy: 0.995 224/3567 [>.............................] - ETA: 17:18 - loss: 0.0233 - accuracy: 0.995 240/3567 [=>............................] - ETA: 17:13 - loss: 0.0228 - accuracy: 0.995 256/3567 [=>............................] - ETA: 17:08 - loss: 0.0225 - accuracy: 0.996 272/3567 [=>............................] - ETA: 17:03 - loss: 0.0221 - accuracy: 0.996 288/3567 [=>............................] - ETA: 16:58 - loss: 0.0230 - accuracy: 0.996 304/3567 [=>............................] - ETA: 16:53 - loss: 0.0230 - accuracy: 0.996 320/3567 [=>............................] - ETA: 16:48 - loss: 0.0232 - accuracy: 0.996 336/3567 [=>............................] - ETA: 16:43 - loss: 0.0229 - accuracy: 0.997 352/3567 [=>............................] - ETA: 16:38 - loss: 0.0226 - accuracy: 0.997 368/3567 [==>...........................] - ETA: 16:34 - loss: 0.0224 - accuracy: 0.997 384/3567 [==>...........................] - ETA: 16:29 - loss: 0.0226 - accuracy: 0.997 400/3567 [==>...........................] - ETA: 16:24 - loss: 0.0223 - accuracy: 0.997 416/3567 [==>...........................] - ETA: 16:19 - loss: 0.0221 - accuracy: 0.997 432/3567 [==>...........................] - ETA: 16:13 - loss: 0.0219 - accuracy: 0.997 448/3567 [==>...........................] - ETA: 16:08 - loss: 0.0216 - accuracy: 0.997 464/3567 [==>...........................] - ETA: 16:03 - loss: 0.0214 - accuracy: 0.997 480/3567 [===>..........................] - ETA: 15:58 - loss: 0.0212 - accuracy: 0.997 496/3567 [===>..........................] - ETA: 15:54 - loss: 0.0211 - accuracy: 0.998 512/3567 [===>..........................] - ETA: 15:49 - loss: 0.0209 - accuracy: 0.998 528/3567 [===>..........................] - ETA: 15:44 - loss: 0.0208 - accuracy: 0.998 544/3567 [===>..........................] - ETA: 15:39 - loss: 0.0206 - accuracy: 0.998 560/3567 [===>..........................] - ETA: 15:34 - loss: 0.0205 - accuracy: 0.998 576/3567 [===>..........................] - ETA: 15:29 - loss: 0.0203 - accuracy: 0.998 592/3567 [===>..........................] - ETA: 15:24 - loss: 0.0202 - accuracy: 0.998 608/3567 [====>.........................] - ETA: 15:19 - loss: 0.0201 - accuracy: 0.998 624/3567 [====>.........................] - ETA: 15:14 - loss: 0.0200 - accuracy: 0.998 640/3567 [====>.........................] - ETA: 15:09 - loss: 0.0199 - accuracy: 0.998 656/3567 [====>.........................] - ETA: 15:04 - loss: 0.0198 - accuracy: 0.998 672/3567 [====>.........................] - ETA: 14:59 - loss: 0.0197 - accuracy: 0.998 688/3567 [====>.........................] - ETA: 14:54 - loss: 0.0196 - accuracy: 0.998 704/3567 [====>.........................] - ETA: 14:49 - loss: 0.0197 - accuracy: 0.998 720/3567 [=====>........................] - ETA: 14:44 - loss: 0.0197 - accuracy: 0.998 736/3567 [=====>........................] - ETA: 14:39 - loss: 0.0196 - accuracy: 0.998 752/3567 [=====>........................] - ETA: 14:34 - loss: 0.0195 - accuracy: 0.998 768/3567 [=====>........................] - ETA: 14:29 - loss: 0.0194 - accuracy: 0.998 784/3567 [=====>........................] - ETA: 14:24 - loss: 0.0193 - accuracy: 0.998 800/3567 [=====>........................] - ETA: 14:19 - loss: 0.0192 - accuracy: 0.998 816/3567 [=====>........................] - ETA: 14:14 - loss: 0.0192 - accuracy: 0.998 832/3567 [=====>........................] - ETA: 14:09 - loss: 0.0191 - accuracy: 0.998 848/3567 [======>.......................] - ETA: 14:04 - loss: 0.0190 - accuracy: 0.998 864/3567 [======>.......................] - ETA: 13:59 - loss: 0.0190 - accuracy: 0.998 880/3567 [======>.......................] - ETA: 13:54 - loss: 0.0189 - accuracy: 0.998 896/3567 [======>.......................] - ETA: 13:49 - loss: 0.0189 - accuracy: 0.998 912/3567 [======>.......................] - ETA: 13:44 - loss: 0.0188 - accuracy: 0.998 928/3567 [======>.......................] - ETA: 13:39 - loss: 0.0188 - accuracy: 0.998 944/3567 [======>.......................] - ETA: 13:34 - loss: 0.0187 - accuracy: 0.998 960/3567 [=======>......................] - ETA: 13:29 - loss: 0.0187 - accuracy: 0.999 976/3567 [=======>......................] - ETA: 13:24 - loss: 0.0186 - accuracy: 0.999 992/3567 [=======>......................] - ETA: 13:19 - loss: 0.0186 - accuracy: 0.9991008/3567 [=======>......................] - ETA: 13:14 - loss: 0.0192 - accuracy: 0.9991024/3567 [=======>......................] - ETA: 13:09 - loss: 0.0191 - accuracy: 0.9991040/3567 [=======>......................] - ETA: 13:04 - loss: 0.0191 - accuracy: 0.9991056/3567 [=======>......................] - ETA: 12:59 - loss: 0.0190 - accuracy: 0.9991072/3567 [========>.....................] - ETA: 12:54 - loss: 0.0190 - accuracy: 0.9991088/3567 [========>.....................] - ETA: 12:49 - loss: 0.0189 - accuracy: 0.9991104/3567 [========>.....................] - ETA: 12:44 - loss: 0.0189 - accuracy: 0.9991120/3567 [========>.....................] - ETA: 12:39 - loss: 0.0188 - accuracy: 0.9991136/3567 [========>.....................] - ETA: 12:34 - loss: 0.0188 - accuracy: 0.9991152/3567 [========>.....................] - ETA: 12:30 - loss: 0.0189 - accuracy: 0.9991168/3567 [========>.....................] - ETA: 12:25 - loss: 0.0188 - accuracy: 0.9991184/3567 [========>.....................] - ETA: 12:20 - loss: 0.0188 - accuracy: 0.9991200/3567 [=========>....................] - ETA: 12:15 - loss: 0.0190 - accuracy: 0.9991216/3567 [=========>....................] - ETA: 12:10 - loss: 0.0189 - accuracy: 0.9991232/3567 [=========>....................] - ETA: 12:05 - loss: 0.0199 - accuracy: 0.9981248/3567 [=========>....................] - ETA: 12:00 - loss: 0.0198 - accuracy: 0.9981264/3567 [=========>....................] - ETA: 11:55 - loss: 0.0198 - accuracy: 0.9981280/3567 [=========>....................] - ETA: 11:50 - loss: 0.0197 - accuracy: 0.9981296/3567 [=========>....................] - ETA: 11:45 - loss: 0.0197 - accuracy: 0.9981312/3567 [==========>...................] - ETA: 11:40 - loss: 0.0196 - accuracy: 0.9981328/3567 [==========>...................] - ETA: 11:35 - loss: 0.0196 - accuracy: 0.9981344/3567 [==========>...................] - ETA: 11:30 - loss: 0.0198 - accuracy: 0.9981360/3567 [==========>...................] - ETA: 11:25 - loss: 0.0198 - accuracy: 0.9981376/3567 [==========>...................] - ETA: 11:20 - loss: 0.0198 - accuracy: 0.9981392/3567 [==========>...................] - ETA: 11:15 - loss: 0.0198 - accuracy: 0.9981408/3567 [==========>...................] - ETA: 11:10 - loss: 0.0197 - accuracy: 0.9981424/3567 [==========>...................] - ETA: 11:05 - loss: 0.0197 - accuracy: 0.9981440/3567 [===========>..................] - ETA: 11:00 - loss: 0.0196 - accuracy: 0.9981456/3567 [===========>..................] - ETA: 10:55 - loss: 0.0225 - accuracy: 0.9971472/3567 [===========>..................] - ETA: 10:50 - loss: 0.0255 - accuracy: 0.9971488/3567 [===========>..................] - ETA: 10:45 - loss: 0.0254 - accuracy: 0.9971504/3567 [===========>..................] - ETA: 10:40 - loss: 0.0253 - accuracy: 0.9971520/3567 [===========>..................] - ETA: 10:35 - loss: 0.0252 - accuracy: 0.9971536/3567 [===========>..................] - ETA: 10:30 - loss: 0.0251 - accuracy: 0.9971552/3567 [============>.................] - ETA: 10:25 - loss: 0.0250 - accuracy: 0.9971568/3567 [============>.................] - ETA: 10:20 - loss: 0.0250 - accuracy: 0.9971584/3567 [============>.................] - ETA: 10:15 - loss: 0.0275 - accuracy: 0.9961600/3567 [============>.................] - ETA: 10:10 - loss: 0.0274 - accuracy: 0.9961616/3567 [============>.................] - ETA: 10:05 - loss: 0.0272 - accuracy: 0.9961632/3567 [============>.................] - ETA: 10:01 - loss: 0.0271 - accuracy: 0.9961648/3567 [============>.................] - ETA: 9:56 - loss: 0.0296 - accuracy: 0.99643567/3567 [==============================] - 1147s 322ms/step - loss: 0.0350 - accuracy: 0.9941 - val_loss: 0.1213 - val_accuracy: 0.9697
  36.  
  37.  
  38.  
  39. Epoch 13/17
  40. 16/3567 [..............................] - ETA: 18:22 - loss: 0.0278 - accuracy: 1.000 32/3567 [..............................] - ETA: 18:15 - loss: 0.0257 - accuracy: 1.000 48/3567 [..............................] - ETA: 18:12 - loss: 0.0223 - accuracy: 1.000 64/3567 [..............................] - ETA: 18:07 - loss: 0.0206 - accuracy: 1.000 80/3567 [..............................] - ETA: 18:02 - loss: 0.0201 - accuracy: 1.000 96/3567 [..............................] - ETA: 17:57 - loss: 0.0196 - accuracy: 1.000 112/3567 [..............................] - ETA: 17:52 - loss: 0.0192 - accuracy: 1.000 128/3567 [>.............................] - ETA: 17:48 - loss: 0.0187 - accuracy: 1.000 144/3567 [>.............................] - ETA: 17:43 - loss: 0.0200 - accuracy: 1.000 160/3567 [>.............................] - ETA: 17:38 - loss: 0.0199 - accuracy: 1.000 176/3567 [>.............................] - ETA: 17:32 - loss: 0.0221 - accuracy: 1.000 192/3567 [>.............................] - ETA: 17:27 - loss: 0.0246 - accuracy: 1.000 208/3567 [>.............................] - ETA: 17:22 - loss: 0.0245 - accuracy: 1.000 224/3567 [>.............................] - ETA: 17:17 - loss: 0.0238 - accuracy: 1.000 240/3567 [=>............................] - ETA: 17:12 - loss: 0.0363 - accuracy: 0.995 256/3567 [=>............................] - ETA: 17:07 - loss: 0.0351 - accuracy: 0.996 272/3567 [=>............................] - ETA: 17:03 - loss: 0.0340 - accuracy: 0.996 288/3567 [=>............................] - ETA: 16:57 - loss: 0.0473 - accuracy: 0.993 304/3567 [=>............................] - ETA: 16:52 - loss: 0.0496 - accuracy: 0.990 320/3567 [=>............................] - ETA: 16:47 - loss: 0.0478 - accuracy: 0.990 336/3567 [=>............................] - ETA: 16:42 - loss: 0.0463 - accuracy: 0.991 352/3567 [=>............................] - ETA: 16:38 - loss: 0.0449 - accuracy: 0.991 368/3567 [==>...........................] - ETA: 16:33 - loss: 0.0436 - accuracy: 0.991 384/3567 [==>...........................] - ETA: 16:28 - loss: 0.0424 - accuracy: 0.992 400/3567 [==>...........................] - ETA: 16:23 - loss: 0.0424 - accuracy: 0.992 416/3567 [==>...........................] - ETA: 16:18 - loss: 0.0414 - accuracy: 0.992 432/3567 [==>...........................] - ETA: 16:13 - loss: 0.0404 - accuracy: 0.993 448/3567 [==>...........................] - ETA: 16:08 - loss: 0.0397 - accuracy: 0.993 464/3567 [==>...........................] - ETA: 16:03 - loss: 0.0388 - accuracy: 0.993 480/3567 [===>..........................] - ETA: 15:58 - loss: 0.0381 - accuracy: 0.993 496/3567 [===>..........................] - ETA: 15:53 - loss: 0.0373 - accuracy: 0.994 512/3567 [===>..........................] - ETA: 15:48 - loss: 0.0367 - accuracy: 0.994 528/3567 [===>..........................] - ETA: 15:43 - loss: 0.0360 - accuracy: 0.994 544/3567 [===>..........................] - ETA: 15:38 - loss: 0.0354 - accuracy: 0.994 560/3567 [===>..........................] - ETA: 15:33 - loss: 0.0349 - accuracy: 0.994 576/3567 [===>..........................] - ETA: 15:28 - loss: 0.0343 - accuracy: 0.994 592/3567 [===>..........................] - ETA: 15:23 - loss: 0.0359 - accuracy: 0.993 608/3567 [====>.........................] - ETA: 15:18 - loss: 0.0353 - accuracy: 0.993 624/3567 [====>.........................] - ETA: 15:13 - loss: 0.0359 - accuracy: 0.993 640/3567 [====>.........................] - ETA: 15:08 - loss: 0.0354 - accuracy: 0.993 656/3567 [====>.........................] - ETA: 15:03 - loss: 0.0349 - accuracy: 0.993 672/3567 [====>.........................] - ETA: 14:58 - loss: 0.0368 - accuracy: 0.992 688/3567 [====>.........................] - ETA: 14:53 - loss: 0.0363 - accuracy: 0.992 704/3567 [====>.........................] - ETA: 14:48 - loss: 0.0358 - accuracy: 0.992 720/3567 [=====>........................] - ETA: 14:43 - loss: 0.0353 - accuracy: 0.993 736/3567 [=====>........................] - ETA: 14:39 - loss: 0.0407 - accuracy: 0.991 752/3567 [=====>........................] - ETA: 14:34 - loss: 0.0401 - accuracy: 0.992 768/3567 [=====>........................] - ETA: 14:29 - loss: 0.0396 - accuracy: 0.992 784/3567 [=====>........................] - ETA: 14:24 - loss: 0.0404 - accuracy: 0.991 800/3567 [=====>........................] - ETA: 14:19 - loss: 0.0399 - accuracy: 0.991 816/3567 [=====>........................] - ETA: 14:14 - loss: 0.0399 - accuracy: 0.991 832/3567 [=====>........................] - ETA: 14:09 - loss: 0.0395 - accuracy: 0.991 848/3567 [======>.......................] - ETA: 14:04 - loss: 0.0390 - accuracy: 0.991 864/3567 [======>.......................] - ETA: 13:59 - loss: 0.0398 - accuracy: 0.990 880/3567 [======>.......................] - ETA: 13:54 - loss: 0.0398 - accuracy: 0.990 896/3567 [======>.......................] - ETA: 13:49 - loss: 0.0393 - accuracy: 0.991 912/3567 [======>.......................] - ETA: 13:44 - loss: 0.0389 - accuracy: 0.991 928/3567 [======>.......................] - ETA: 13:39 - loss: 0.0385 - accuracy: 0.991 944/3567 [======>.......................] - ETA: 13:34 - loss: 0.0381 - accuracy: 0.991 960/3567 [=======>......................] - ETA: 13:29 - loss: 0.0378 - accuracy: 0.991 976/3567 [=======>......................] - ETA: 13:24 - loss: 0.0374 - accuracy: 0.991 992/3567 [=======>......................] - ETA: 13:19 - loss: 0.0372 - accuracy: 0.9911008/3567 [=======>......................] - ETA: 13:14 - loss: 0.0369 - accuracy: 0.9921024/3567 [=======>......................] - ETA: 13:09 - loss: 0.0365 - accuracy: 0.9921040/3567 [=======>......................] - ETA: 13:04 - loss: 0.0363 - accuracy: 0.9921056/3567 [=======>......................] - ETA: 12:59 - loss: 0.0359 - accuracy: 0.9921072/3567 [========>.....................] - ETA: 12:54 - loss: 0.0356 - accuracy: 0.9921088/3567 [========>.....................] - ETA: 12:49 - loss: 0.0362 - accuracy: 0.9911104/3567 [========>.....................] - ETA: 12:44 - loss: 0.0359 - accuracy: 0.9911120/3567 [========>.....................] - ETA: 12:39 - loss: 0.0356 - accuracy: 0.9921136/3567 [========>.....................] - ETA: 12:34 - loss: 0.0353 - accuracy: 0.9921152/3567 [========>.....................] - ETA: 12:29 - loss: 0.0387 - accuracy: 0.9911168/3567 [========>.....................] - ETA: 12:24 - loss: 0.0419 - accuracy: 0.9901184/3567 [========>.....................] - ETA: 12:20 - loss: 0.0416 - accuracy: 0.9901200/3567 [=========>....................] - ETA: 12:15 - loss: 0.0413 - accuracy: 0.9901216/3567 [=========>....................] - ETA: 12:10 - loss: 0.0409 - accuracy: 0.9911232/3567 [=========>....................] - ETA: 12:05 - loss: 0.0407 - accuracy: 0.9911248/3567 [=========>....................] - ETA: 12:00 - loss: 0.0437 - accuracy: 0.9901264/3567 [=========>....................] - ETA: 11:55 - loss: 0.0439 - accuracy: 0.9901280/3567 [=========>....................] - ETA: 11:50 - loss: 0.0436 - accuracy: 0.9901296/3567 [=========>....................] - ETA: 11:45 - loss: 0.0432 - accuracy: 0.9901312/3567 [==========>...................] - ETA: 11:40 - loss: 0.0431 - accuracy: 0.9901328/3567 [==========>...................] - ETA: 11:35 - loss: 0.0428 - accuracy: 0.9911344/3567 [==========>...................] - ETA: 11:30 - loss: 0.0456 - accuracy: 0.9901360/3567 [==========>...................] - ETA: 11:25 - loss: 0.0453 - accuracy: 0.9901376/3567 [==========>...................] - ETA: 11:20 - loss: 0.0449 - accuracy: 0.9901392/3567 [==========>...................] - ETA: 11:15 - loss: 0.0447 - accuracy: 0.9901408/3567 [==========>...................] - ETA: 11:10 - loss: 0.0444 - accuracy: 0.9901424/3567 [==========>...................] - ETA: 11:05 - loss: 0.0441 - accuracy: 0.9901440/3567 [===========>..................] - ETA: 11:00 - loss: 0.0438 - accuracy: 0.9911456/3567 [===========>..................] - ETA: 10:55 - loss: 0.0434 - accuracy: 0.9911472/3567 [===========>..................] - ETA: 10:50 - loss: 0.0431 - accuracy: 0.9911488/3567 [===========>..................] - ETA: 10:45 - loss: 0.0432 - accuracy: 0.9911504/3567 [===========>..................] - ETA: 10:40 - loss: 0.0429 - accuracy: 0.9911520/3567 [===========>..................] - ETA: 10:35 - loss: 0.0426 - accuracy: 0.9911536/3567 [===========>..................] - ETA: 10:30 - loss: 0.0423 - accuracy: 0.9911552/3567 [============>.................] - ETA: 10:25 - loss: 0.0420 - accuracy: 0.9911568/3567 [============>.................] - ETA: 10:20 - loss: 0.0418 - accuracy: 0.9911584/3567 [============>.................] - ETA: 10:15 - loss: 0.0415 - accuracy: 0.9911600/3567 [============>.................] - ETA: 10:10 - loss: 0.0412 - accuracy: 0.9911616/3567 [============>.................] - ETA: 10:05 - loss: 0.0410 - accuracy: 0.9921632/3567 [============>.................] - ETA: 10:00 - loss: 0.0408 - accuracy: 0.9921648/3567 [============>.................] - ETA: 9:55 - loss: 0.0405 - accuracy: 0.99213567/3567 [==============================] - 1147s 322ms/step - loss: 0.0399 - accuracy: 0.9919 - val_loss: 0.0840 - val_accuracy: 0.9697
  41.  
  42.  
  43.  
  44. Epoch 14/17
  45. 16/3567 [..............................] - ETA: 18:22 - loss: 0.1582 - accuracy: 0.937 32/3567 [..............................] - ETA: 18:18 - loss: 0.0868 - accuracy: 0.968 48/3567 [..............................] - ETA: 18:12 - loss: 0.0729 - accuracy: 0.979 64/3567 [..............................] - ETA: 18:08 - loss: 0.0584 - accuracy: 0.984 80/3567 [..............................] - ETA: 18:02 - loss: 0.0497 - accuracy: 0.987 96/3567 [..............................] - ETA: 17:58 - loss: 0.0765 - accuracy: 0.979 112/3567 [..............................] - ETA: 17:53 - loss: 0.0678 - accuracy: 0.982 128/3567 [>.............................] - ETA: 17:48 - loss: 0.0612 - accuracy: 0.984 144/3567 [>.............................] - ETA: 17:43 - loss: 0.0561 - accuracy: 0.986 160/3567 [>.............................] - ETA: 17:38 - loss: 0.0520 - accuracy: 0.987 176/3567 [>.............................] - ETA: 17:33 - loss: 0.0488 - accuracy: 0.988 192/3567 [>.............................] - ETA: 17:28 - loss: 0.0661 - accuracy: 0.984 208/3567 [>.............................] - ETA: 17:23 - loss: 0.1045 - accuracy: 0.976 224/3567 [>.............................] - ETA: 17:18 - loss: 0.0982 - accuracy: 0.977 240/3567 [=>............................] - ETA: 17:13 - loss: 0.0953 - accuracy: 0.979 256/3567 [=>............................] - ETA: 17:08 - loss: 0.0903 - accuracy: 0.980 272/3567 [=>............................] - ETA: 17:03 - loss: 0.0886 - accuracy: 0.981 288/3567 [=>............................] - ETA: 16:58 - loss: 0.0845 - accuracy: 0.982 304/3567 [=>............................] - ETA: 16:53 - loss: 0.0808 - accuracy: 0.983 320/3567 [=>............................] - ETA: 16:48 - loss: 0.0910 - accuracy: 0.981 336/3567 [=>............................] - ETA: 16:43 - loss: 0.0875 - accuracy: 0.982 352/3567 [=>............................] - ETA: 16:38 - loss: 0.0842 - accuracy: 0.983 368/3567 [==>...........................] - ETA: 16:33 - loss: 0.0853 - accuracy: 0.981 384/3567 [==>...........................] - ETA: 16:28 - loss: 0.0825 - accuracy: 0.981 400/3567 [==>...........................] - ETA: 16:23 - loss: 0.0801 - accuracy: 0.982 416/3567 [==>...........................] - ETA: 16:18 - loss: 0.0776 - accuracy: 0.983 432/3567 [==>...........................] - ETA: 16:13 - loss: 0.0753 - accuracy: 0.983 448/3567 [==>...........................] - ETA: 16:08 - loss: 0.0744 - accuracy: 0.984 464/3567 [==>...........................] - ETA: 16:03 - loss: 0.0725 - accuracy: 0.984 480/3567 [===>..........................] - ETA: 15:58 - loss: 0.0706 - accuracy: 0.985 496/3567 [===>..........................] - ETA: 15:53 - loss: 0.0694 - accuracy: 0.985 512/3567 [===>..........................] - ETA: 15:48 - loss: 0.0686 - accuracy: 0.986 528/3567 [===>..........................] - ETA: 15:43 - loss: 0.0670 - accuracy: 0.986 544/3567 [===>..........................] - ETA: 15:38 - loss: 0.0655 - accuracy: 0.987 560/3567 [===>..........................] - ETA: 15:33 - loss: 0.0650 - accuracy: 0.987 576/3567 [===>..........................] - 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ETA: 14:29 - loss: 0.0532 - accuracy: 0.989 784/3567 [=====>........................] - ETA: 14:24 - loss: 0.0528 - accuracy: 0.989 800/3567 [=====>........................] - ETA: 14:19 - loss: 0.0520 - accuracy: 0.990 816/3567 [=====>........................] - ETA: 14:14 - loss: 0.0513 - accuracy: 0.990 832/3567 [=====>........................] - ETA: 14:09 - loss: 0.0506 - accuracy: 0.990 848/3567 [======>.......................] - ETA: 14:04 - loss: 0.0499 - accuracy: 0.990 864/3567 [======>.......................] - ETA: 13:59 - loss: 0.0493 - accuracy: 0.990 880/3567 [======>.......................] - ETA: 13:54 - loss: 0.0487 - accuracy: 0.990 896/3567 [======>.......................] - ETA: 13:50 - loss: 0.0481 - accuracy: 0.991 912/3567 [======>.......................] - ETA: 13:45 - loss: 0.0475 - accuracy: 0.991 928/3567 [======>.......................] - ETA: 13:40 - loss: 0.0470 - accuracy: 0.991 944/3567 [======>.......................] - ETA: 13:35 - loss: 0.0464 - accuracy: 0.991 960/3567 [=======>......................] - ETA: 13:30 - loss: 0.0459 - accuracy: 0.991 976/3567 [=======>......................] - ETA: 13:25 - loss: 0.0498 - accuracy: 0.990 992/3567 [=======>......................] - ETA: 13:20 - loss: 0.0495 - accuracy: 0.9901008/3567 [=======>......................] - ETA: 13:15 - loss: 0.0490 - accuracy: 0.9911024/3567 [=======>......................] - ETA: 13:10 - loss: 0.0487 - accuracy: 0.9911040/3567 [=======>......................] - ETA: 13:05 - loss: 0.0482 - accuracy: 0.9911056/3567 [=======>......................] - ETA: 13:00 - loss: 0.0517 - accuracy: 0.9901072/3567 [========>.....................] - ETA: 12:55 - loss: 0.0511 - accuracy: 0.9901088/3567 [========>.....................] - ETA: 12:50 - loss: 0.0508 - accuracy: 0.9901104/3567 [========>.....................] - ETA: 12:45 - loss: 0.0502 - accuracy: 0.9901120/3567 [========>.....................] - ETA: 12:40 - loss: 0.0497 - accuracy: 0.9911136/3567 [========>.....................] - ETA: 12:35 - loss: 0.0493 - accuracy: 0.9911152/3567 [========>.....................] - ETA: 12:30 - loss: 0.0488 - accuracy: 0.9911168/3567 [========>.....................] - ETA: 12:25 - loss: 0.0483 - accuracy: 0.9911184/3567 [========>.....................] - ETA: 12:20 - loss: 0.0514 - accuracy: 0.9901200/3567 [=========>....................] - ETA: 12:15 - loss: 0.0511 - accuracy: 0.9901216/3567 [=========>....................] - ETA: 12:11 - loss: 0.0506 - accuracy: 0.9911232/3567 [=========>....................] - ETA: 12:05 - loss: 0.0505 - accuracy: 0.9911248/3567 [=========>....................] - ETA: 12:00 - loss: 0.0500 - accuracy: 0.9911264/3567 [=========>....................] - ETA: 11:56 - loss: 0.0496 - accuracy: 0.9911280/3567 [=========>....................] - ETA: 11:51 - loss: 0.0492 - accuracy: 0.9911296/3567 [=========>....................] - ETA: 11:46 - loss: 0.0487 - accuracy: 0.9911312/3567 [==========>...................] - ETA: 11:41 - loss: 0.0483 - accuracy: 0.9911328/3567 [==========>...................] - ETA: 11:36 - loss: 0.0479 - accuracy: 0.9911344/3567 [==========>...................] - ETA: 11:31 - loss: 0.0475 - accuracy: 0.9911360/3567 [==========>...................] - ETA: 11:26 - loss: 0.0472 - accuracy: 0.9911376/3567 [==========>...................] - ETA: 11:21 - loss: 0.0468 - accuracy: 0.9921392/3567 [==========>...................] - ETA: 11:16 - loss: 0.0464 - accuracy: 0.9921408/3567 [==========>...................] - ETA: 11:11 - loss: 0.0461 - accuracy: 0.9921424/3567 [==========>...................] - ETA: 11:06 - loss: 0.0457 - accuracy: 0.9921440/3567 [===========>..................] - ETA: 11:01 - loss: 0.0454 - accuracy: 0.9921456/3567 [===========>..................] - ETA: 10:56 - loss: 0.0450 - accuracy: 0.9921472/3567 [===========>..................] - ETA: 10:51 - loss: 0.0448 - accuracy: 0.9921488/3567 [===========>..................] - ETA: 10:46 - loss: 0.0445 - accuracy: 0.9921504/3567 [===========>..................] - ETA: 10:41 - loss: 0.0441 - accuracy: 0.9921520/3567 [===========>..................] - ETA: 10:36 - loss: 0.0438 - accuracy: 0.9921536/3567 [===========>..................] - ETA: 10:31 - loss: 0.0435 - accuracy: 0.9921552/3567 [============>.................] - ETA: 10:26 - loss: 0.0439 - accuracy: 0.9921568/3567 [============>.................] - ETA: 10:21 - loss: 0.0436 - accuracy: 0.9921584/3567 [============>.................] - ETA: 10:16 - loss: 0.0433 - accuracy: 0.9921600/3567 [============>.................] - ETA: 10:11 - loss: 0.0431 - accuracy: 0.9921616/3567 [============>.................] - ETA: 10:06 - loss: 0.0429 - accuracy: 0.9921632/3567 [============>.................] - ETA: 10:01 - loss: 0.0439 - accuracy: 0.9921648/3567 [============>.................] - ETA: 9:56 - loss: 0.0437 - accuracy: 0.99213567/3567 [==============================] - 1148s 322ms/step - loss: 0.0426 - accuracy: 0.9924 - val_loss: 0.0418 - val_accuracy: 0.9949
  46.  
  47.  
  48.  
  49. Epoch 15/17
  50. 16/3567 [..............................] - ETA: 18:17 - loss: 0.0146 - accuracy: 1.000 32/3567 [..............................] - ETA: 18:14 - loss: 0.0166 - accuracy: 1.000 48/3567 [..............................] - ETA: 18:11 - loss: 0.0160 - accuracy: 1.000 64/3567 [..............................] - ETA: 18:07 - loss: 0.0157 - accuracy: 1.000 80/3567 [..............................] - ETA: 18:03 - loss: 0.0155 - accuracy: 1.000 96/3567 [..............................] - ETA: 17:58 - loss: 0.0154 - accuracy: 1.000 112/3567 [..............................] - ETA: 17:52 - loss: 0.0153 - accuracy: 1.000 128/3567 [>.............................] - ETA: 17:48 - loss: 0.0152 - accuracy: 1.000 144/3567 [>.............................] - ETA: 17:43 - loss: 0.0152 - accuracy: 1.000 160/3567 [>.............................] - ETA: 17:38 - loss: 0.0151 - accuracy: 1.000 176/3567 [>.............................] - ETA: 17:33 - loss: 0.0151 - accuracy: 1.000 192/3567 [>.............................] - ETA: 17:28 - loss: 0.0151 - accuracy: 1.000 208/3567 [>.............................] - ETA: 17:24 - loss: 0.0150 - accuracy: 1.000 224/3567 [>.............................] - ETA: 17:18 - loss: 0.0150 - accuracy: 1.000 240/3567 [=>............................] - ETA: 17:13 - loss: 0.0150 - accuracy: 1.000 256/3567 [=>............................] - ETA: 17:08 - loss: 0.0150 - accuracy: 1.000 272/3567 [=>............................] - ETA: 17:03 - loss: 0.0150 - accuracy: 1.000 288/3567 [=>............................] - ETA: 16:58 - loss: 0.0150 - accuracy: 1.000 304/3567 [=>............................] - ETA: 16:53 - loss: 0.0150 - accuracy: 1.000 320/3567 [=>............................] - ETA: 16:48 - loss: 0.0280 - accuracy: 0.996 336/3567 [=>............................] - ETA: 16:43 - loss: 0.0274 - accuracy: 0.997 352/3567 [=>............................] - ETA: 16:38 - loss: 0.0268 - accuracy: 0.997 368/3567 [==>...........................] - ETA: 16:33 - loss: 0.0264 - accuracy: 0.997 384/3567 [==>...........................] - ETA: 16:28 - loss: 0.0259 - accuracy: 0.997 400/3567 [==>...........................] - ETA: 16:23 - loss: 0.0255 - accuracy: 0.997 416/3567 [==>...........................] - ETA: 16:18 - loss: 0.0251 - accuracy: 0.997 432/3567 [==>...........................] - ETA: 16:14 - loss: 0.0247 - accuracy: 0.997 448/3567 [==>...........................] - ETA: 16:09 - loss: 0.0243 - accuracy: 0.997 464/3567 [==>...........................] - ETA: 16:04 - loss: 0.0240 - accuracy: 0.997 480/3567 [===>..........................] - ETA: 15:59 - loss: 0.0238 - accuracy: 0.997 496/3567 [===>..........................] - ETA: 15:54 - loss: 0.0235 - accuracy: 0.998 512/3567 [===>..........................] - ETA: 15:49 - loss: 0.0232 - accuracy: 0.998 528/3567 [===>..........................] - ETA: 15:44 - loss: 0.0230 - accuracy: 0.998 544/3567 [===>..........................] - ETA: 15:38 - loss: 0.0227 - accuracy: 0.998 560/3567 [===>..........................] - ETA: 15:33 - loss: 0.0225 - accuracy: 0.998 576/3567 [===>..........................] - ETA: 15:28 - loss: 0.0223 - accuracy: 0.998 592/3567 [===>..........................] - ETA: 15:24 - loss: 0.0220 - accuracy: 0.998 608/3567 [====>.........................] - ETA: 15:18 - loss: 0.0218 - accuracy: 0.998 624/3567 [====>.........................] - ETA: 15:13 - loss: 0.0285 - accuracy: 0.996 640/3567 [====>.........................] - ETA: 15:08 - loss: 0.0297 - accuracy: 0.995 656/3567 [====>.........................] - ETA: 15:04 - loss: 0.0293 - accuracy: 0.995 672/3567 [====>.........................] - ETA: 14:59 - loss: 0.0291 - accuracy: 0.995 688/3567 [====>.........................] - ETA: 14:54 - loss: 0.0287 - accuracy: 0.995 704/3567 [====>.........................] - ETA: 14:49 - loss: 0.0284 - accuracy: 0.995 720/3567 [=====>........................] - ETA: 14:44 - loss: 0.0281 - accuracy: 0.995 736/3567 [=====>........................] - ETA: 14:39 - loss: 0.0278 - accuracy: 0.995 752/3567 [=====>........................] - ETA: 14:34 - loss: 0.0275 - accuracy: 0.996 768/3567 [=====>........................] - ETA: 14:29 - loss: 0.0273 - accuracy: 0.996 784/3567 [=====>........................] - ETA: 14:24 - loss: 0.0270 - accuracy: 0.996 800/3567 [=====>........................] - ETA: 14:19 - loss: 0.0320 - accuracy: 0.995 816/3567 [=====>........................] - ETA: 14:14 - loss: 0.0316 - accuracy: 0.995 832/3567 [=====>........................] - ETA: 14:09 - loss: 0.0313 - accuracy: 0.995 848/3567 [======>.......................] - ETA: 14:04 - loss: 0.0310 - accuracy: 0.995 864/3567 [======>.......................] - ETA: 13:59 - loss: 0.0307 - accuracy: 0.995 880/3567 [======>.......................] - ETA: 13:54 - loss: 0.0304 - accuracy: 0.995 896/3567 [======>.......................] - ETA: 13:49 - loss: 0.0301 - accuracy: 0.995 912/3567 [======>.......................] - ETA: 13:44 - loss: 0.0349 - accuracy: 0.994 928/3567 [======>.......................] - ETA: 13:39 - loss: 0.0345 - accuracy: 0.994 944/3567 [======>.......................] - ETA: 13:34 - loss: 0.0342 - accuracy: 0.994 960/3567 [=======>......................] - ETA: 13:29 - loss: 0.0339 - accuracy: 0.994 976/3567 [=======>......................] - ETA: 13:24 - loss: 0.0336 - accuracy: 0.994 992/3567 [=======>......................] - ETA: 13:19 - loss: 0.0333 - accuracy: 0.9951008/3567 [=======>......................] - ETA: 13:14 - loss: 0.0330 - accuracy: 0.9951024/3567 [=======>......................] - ETA: 13:09 - loss: 0.0327 - accuracy: 0.9951040/3567 [=======>......................] - ETA: 13:04 - loss: 0.0328 - accuracy: 0.9951056/3567 [=======>......................] - ETA: 12:59 - loss: 0.0325 - accuracy: 0.9951072/3567 [========>.....................] - ETA: 12:54 - loss: 0.0322 - accuracy: 0.9951088/3567 [========>.....................] - ETA: 12:49 - loss: 0.0320 - accuracy: 0.9951104/3567 [========>.....................] - ETA: 12:44 - loss: 0.0317 - accuracy: 0.9951120/3567 [========>.....................] - ETA: 12:39 - loss: 0.0315 - accuracy: 0.9951136/3567 [========>.....................] - ETA: 12:34 - loss: 0.0312 - accuracy: 0.9951152/3567 [========>.....................] - ETA: 12:29 - loss: 0.0310 - accuracy: 0.9951168/3567 [========>.....................] - ETA: 12:24 - loss: 0.0308 - accuracy: 0.9951184/3567 [========>.....................] - ETA: 12:19 - loss: 0.0306 - accuracy: 0.9951200/3567 [=========>....................] - ETA: 12:14 - loss: 0.0304 - accuracy: 0.9951216/3567 [=========>....................] - ETA: 12:09 - loss: 0.0302 - accuracy: 0.9951232/3567 [=========>....................] - ETA: 12:04 - loss: 0.0300 - accuracy: 0.9951248/3567 [=========>....................] - ETA: 12:00 - loss: 0.0298 - accuracy: 0.9961264/3567 [=========>....................] - ETA: 11:55 - loss: 0.0296 - accuracy: 0.9961280/3567 [=========>....................] - ETA: 11:50 - loss: 0.0294 - accuracy: 0.9961296/3567 [=========>....................] - ETA: 11:45 - loss: 0.0292 - accuracy: 0.9961312/3567 [==========>...................] - ETA: 11:40 - loss: 0.0290 - accuracy: 0.9961328/3567 [==========>...................] - ETA: 11:35 - loss: 0.0289 - accuracy: 0.9961344/3567 [==========>...................] - ETA: 11:30 - loss: 0.0287 - accuracy: 0.9961360/3567 [==========>...................] - ETA: 11:25 - loss: 0.0285 - accuracy: 0.9961376/3567 [==========>...................] - ETA: 11:20 - loss: 0.0284 - accuracy: 0.9961392/3567 [==========>...................] - ETA: 11:15 - loss: 0.0282 - accuracy: 0.9961408/3567 [==========>...................] - ETA: 11:10 - loss: 0.0281 - accuracy: 0.9961424/3567 [==========>...................] - ETA: 11:05 - loss: 0.0279 - accuracy: 0.9961440/3567 [===========>..................] - ETA: 11:00 - loss: 0.0278 - accuracy: 0.9961456/3567 [===========>..................] - ETA: 10:55 - loss: 0.0276 - accuracy: 0.9961472/3567 [===========>..................] - ETA: 10:50 - loss: 0.0275 - accuracy: 0.9961488/3567 [===========>..................] - ETA: 10:45 - loss: 0.0273 - accuracy: 0.9961504/3567 [===========>..................] - ETA: 10:40 - loss: 0.0272 - accuracy: 0.9961520/3567 [===========>..................] - ETA: 10:35 - loss: 0.0271 - accuracy: 0.9961536/3567 [===========>..................] - ETA: 10:30 - loss: 0.0270 - accuracy: 0.9961552/3567 [============>.................] - ETA: 10:25 - loss: 0.0268 - accuracy: 0.9961568/3567 [============>.................] - ETA: 10:20 - loss: 0.0267 - accuracy: 0.9961584/3567 [============>.................] - ETA: 10:15 - loss: 0.0266 - accuracy: 0.9961600/3567 [============>.................] - ETA: 10:10 - loss: 0.0265 - accuracy: 0.9961616/3567 [============>.................] - ETA: 10:05 - loss: 0.0263 - accuracy: 0.9961632/3567 [============>.................] - ETA: 10:00 - loss: 0.0262 - accuracy: 0.9961648/3567 [============>.................] - ETA: 9:55 - loss: 0.0261 - accuracy: 0.99703567/3567 [==============================] - 1147s 322ms/step - loss: 0.0236 - accuracy: 0.9978 - val_loss: 0.0324 - val_accuracy: 0.9949
  51.  
  52.  
  53.  
  54.  
  55. Epoch 16/17
  56. 16/3567 [..............................] - ETA: 18:20 - loss: 0.0142 - accuracy: 1.000 32/3567 [..............................] - ETA: 18:14 - loss: 0.0142 - accuracy: 1.000 48/3567 [..............................] - ETA: 18:10 - loss: 0.0142 - accuracy: 1.000 64/3567 [..............................] - ETA: 18:05 - loss: 0.0142 - accuracy: 1.000 80/3567 [..............................] - ETA: 18:01 - loss: 0.0143 - accuracy: 1.000 96/3567 [..............................] - ETA: 17:56 - loss: 0.0143 - accuracy: 1.000 112/3567 [..............................] - ETA: 17:51 - loss: 0.0143 - accuracy: 1.000 128/3567 [>.............................] - ETA: 17:46 - loss: 0.0142 - accuracy: 1.000 144/3567 [>.............................] - ETA: 17:42 - loss: 0.0142 - accuracy: 1.000 160/3567 [>.............................] - ETA: 17:36 - loss: 0.0144 - accuracy: 1.000 176/3567 [>.............................] - ETA: 17:31 - loss: 0.0144 - accuracy: 1.000 192/3567 [>.............................] - ETA: 17:26 - loss: 0.0171 - accuracy: 1.000 208/3567 [>.............................] - ETA: 17:21 - loss: 0.0168 - accuracy: 1.000 224/3567 [>.............................] - ETA: 17:16 - loss: 0.0167 - accuracy: 1.000 240/3567 [=>............................] - ETA: 17:12 - loss: 0.0165 - accuracy: 1.000 256/3567 [=>............................] - ETA: 17:06 - loss: 0.0164 - accuracy: 1.000 272/3567 [=>............................] - ETA: 17:02 - loss: 0.0162 - accuracy: 1.000 288/3567 [=>............................] - ETA: 16:57 - loss: 0.0161 - accuracy: 1.000 304/3567 [=>............................] - ETA: 16:52 - loss: 0.0160 - accuracy: 1.000 320/3567 [=>............................] - ETA: 16:47 - loss: 0.0160 - accuracy: 1.000 336/3567 [=>............................] - ETA: 16:42 - loss: 0.0159 - accuracy: 1.000 352/3567 [=>............................] - ETA: 16:37 - loss: 0.0158 - accuracy: 1.000 368/3567 [==>...........................] - ETA: 16:32 - loss: 0.0157 - accuracy: 1.000 384/3567 [==>...........................] - ETA: 16:27 - loss: 0.0266 - accuracy: 0.997 400/3567 [==>...........................] - ETA: 16:22 - loss: 0.0261 - accuracy: 0.997 416/3567 [==>...........................] - ETA: 16:17 - loss: 0.0257 - accuracy: 0.997 432/3567 [==>...........................] - ETA: 16:12 - loss: 0.0252 - accuracy: 0.997 448/3567 [==>...........................] - ETA: 16:07 - loss: 0.0248 - accuracy: 0.997 464/3567 [==>...........................] - ETA: 16:02 - loss: 0.0245 - accuracy: 0.997 480/3567 [===>..........................] - ETA: 15:58 - loss: 0.0241 - accuracy: 0.997 496/3567 [===>..........................] - ETA: 15:53 - loss: 0.0238 - accuracy: 0.998 512/3567 [===>..........................] - ETA: 15:48 - loss: 0.0235 - accuracy: 0.998 528/3567 [===>..........................] - ETA: 15:43 - loss: 0.0233 - accuracy: 0.998 544/3567 [===>..........................] - ETA: 15:38 - loss: 0.0231 - accuracy: 0.998 560/3567 [===>..........................] - ETA: 15:33 - loss: 0.0228 - accuracy: 0.998 576/3567 [===>..........................] - 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ETA: 10:30 - loss: 0.0310 - accuracy: 0.9941552/3567 [============>.................] - ETA: 10:25 - loss: 0.0308 - accuracy: 0.9941568/3567 [============>.................] - ETA: 10:20 - loss: 0.0307 - accuracy: 0.9941584/3567 [============>.................] - ETA: 10:15 - loss: 0.0306 - accuracy: 0.9941600/3567 [============>.................] - ETA: 10:10 - loss: 0.0330 - accuracy: 0.9941616/3567 [============>.................] - ETA: 10:05 - loss: 0.0355 - accuracy: 0.9931632/3567 [============>.................] - ETA: 10:00 - loss: 0.0353 - accuracy: 0.9931648/3567 [============>.................] - ETA: 9:55 - loss: 0.0351 - accuracy: 0.99393567/3567 [==============================] - 1147s 321ms/step - loss: 0.0420 - accuracy: 0.9902 - val_loss: 0.0620 - val_accuracy: 0.9848
  57. Epoch 17/17
  58. 16/3567 [..............................] - ETA: 18:18 - loss: 0.0158 - accuracy: 1.000 32/3567 [..............................] - ETA: 18:16 - loss: 0.0149 - accuracy: 1.000 48/3567 [..............................] - ETA: 18:12 - loss: 0.0153 - accuracy: 1.000 64/3567 [..............................] - ETA: 18:06 - loss: 0.0151 - accuracy: 1.000 80/3567 [..............................] - ETA: 18:02 - loss: 0.0285 - accuracy: 0.987 96/3567 [..............................] - ETA: 17:56 - loss: 0.0276 - accuracy: 0.989 112/3567 [..............................] - ETA: 17:51 - loss: 0.0268 - accuracy: 0.991 128/3567 [>.............................] - ETA: 17:46 - loss: 0.0254 - accuracy: 0.992 144/3567 [>.............................] - ETA: 17:41 - loss: 0.0278 - accuracy: 0.993 160/3567 [>.............................] - ETA: 17:36 - loss: 0.0264 - accuracy: 0.993 176/3567 [>.............................] - ETA: 17:31 - loss: 0.0257 - accuracy: 0.994 192/3567 [>.............................] - 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ETA: 10:30 - loss: 0.0456 - accuracy: 0.9881552/3567 [============>.................] - ETA: 10:25 - loss: 0.0453 - accuracy: 0.9881568/3567 [============>.................] - ETA: 10:20 - loss: 0.0450 - accuracy: 0.9881584/3567 [============>.................] - ETA: 10:15 - loss: 0.0447 - accuracy: 0.9881600/3567 [============>.................] - ETA: 10:10 - loss: 0.0448 - accuracy: 0.9881616/3567 [============>.................] - ETA: 10:05 - loss: 0.0445 - accuracy: 0.9881632/3567 [============>.................] - ETA: 10:00 - loss: 0.0442 - accuracy: 0.9881648/3567 [============>.................] - ETA: 9:55 - loss: 0.0439 - accuracy: 0.98853567/3567 [==============================] - 1147s 321ms/step - loss: 0.0345 - accuracy: 0.9933 - val_loss: 0.0402 - val_accuracy: 0.9949
  59.  
  60.  
  61. Train Feature Extraction...
  62. Train feature extraction finished in 392.633906 seconds.
  63. Test feature extraction finished in 97.836162 seconds.
  64. precision recall f1-score support
  65.  
  66. 0 0.99 0.99 0.99 418
  67. 1 0.99 0.99 0.99 376
  68.  
  69. accuracy 0.99 794
  70. macro avg 0.99 0.99 0.99 794
  71. weighted avg 0.99 0.99 0.99 794
  72.  
  73. Acc: 0.9924433249370277 794 788.0
  74. Train Feature Extraction...
  75. Train feature extraction finished in 391.318367 seconds.
  76. Test feature extraction finished in 97.751178 seconds.
  77. precision recall f1-score support
  78.  
  79. 0 1.00 1.00 1.00 418
  80. 1 0.99 0.99 0.99 375
  81.  
  82. accuracy 0.99 793
  83. macro avg 0.99 0.99 0.99 793
  84. weighted avg 0.99 0.99 0.99 793
  85.  
  86. Acc: 0.9949558638083228 793 789.0
  87. Train Feature Extraction...
  88. Train feature extraction finished in 390.829987 seconds.
  89. Test feature extraction finished in 97.771870 seconds.
  90. precision recall f1-score support
  91.  
  92. 0 1.00 1.00 1.00 418
  93. 1 1.00 0.99 1.00 375
  94.  
  95. accuracy 1.00 793
  96. macro avg 1.00 1.00 1.00 793
  97. weighted avg 1.00 1.00 1.00 793
  98.  
  99. Acc: 0.9974779319041615 793 791.0
  100. Train Feature Extraction...
  101. Train feature extraction finished in 390.710775 seconds.
  102. Test feature extraction finished in 97.590359 seconds.
  103. precision recall f1-score support
  104.  
  105. 0 1.00 0.99 1.00 417
  106. 1 0.99 1.00 0.99 375
  107.  
  108. accuracy 0.99 792
  109. macro avg 0.99 1.00 0.99 792
  110. weighted avg 0.99 0.99 0.99 792
  111.  
  112. Acc: 0.9949494949494949 792 788.0
  113. Train Feature Extraction...
  114. Train feature extraction finished in 390.546796 seconds.
  115. Test feature extraction finished in 97.485896 seconds.
  116. precision recall f1-score support
  117.  
  118. 0 1.00 1.00 1.00 417
  119. 1 0.99 0.99 0.99 375
  120.  
  121. accuracy 0.99 792
  122. macro avg 0.99 0.99 0.99 792
  123. weighted avg 0.99 0.99 0.99 792
  124.  
  125. Acc: 0.9949494949494949 792 788.0
  126. End: 3944.0 3964 0.9949545913218971
  127.  
  128.  
  129.  
  130.  
  131.  
  132.  
  133.  
  134. última (97.96%):
  135.  
  136. Train on 3567 samples, validate on 396 samples
  137. Epoch 1/3
  138. 2020-02-06 20:38:51.411822: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 205520896 exceeds 10% of system memory.
  139. 2020-02-06 20:38:51.458498: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 209207296 exceeds 10% of system memory.
  140. 16/3567 [..............................] - ETA: 21:52 - loss: 0.0136 - accuracy: 1.000 32/3567 [..............................] - ETA: 20:45 - loss: 0.0137 - accuracy: 1.000 48/3567 [..............................] - ETA: 20:59 - loss: 0.0137 - accuracy: 1.000 64/3567 [..............................] - ETA: 21:04 - loss: 0.0140 - accuracy: 1.000 80/3567 [..............................] - ETA: 21:05 - loss: 0.0139 - accuracy: 1.000 96/3567 [..............................] - ETA: 21:01 - loss: 0.0138 - accuracy: 1.000 112/3567 [..............................] - ETA: 20:57 - loss: 0.0138 - accuracy: 1.000 128/3567 [>.............................] - ETA: 20:52 - loss: 0.0138 - accuracy: 1.000 144/3567 [>.............................] - ETA: 20:45 - loss: 0.0137 - accuracy: 1.000 160/3567 [>.............................] - ETA: 20:32 - loss: 0.0137 - accuracy: 1.000 176/3567 [>.............................] - ETA: 20:13 - loss: 0.0137 - accuracy: 1.000 192/3567 [>.............................] - ETA: 19:57 - loss: 0.0137 - accuracy: 1.000 208/3567 [>.............................] - ETA: 19:42 - loss: 0.0137 - accuracy: 1.000 224/3567 [>.............................] - ETA: 19:29 - loss: 0.0137 - accuracy: 1.000 240/3567 [=>............................] - ETA: 19:17 - loss: 0.0137 - accuracy: 1.000 256/3567 [=>............................] - ETA: 19:06 - loss: 0.0137 - accuracy: 1.000 272/3567 [=>............................] - ETA: 18:55 - loss: 0.0137 - accuracy: 1.000 288/3567 [=>............................] - ETA: 18:45 - loss: 0.0137 - accuracy: 1.000 304/3567 [=>............................] - ETA: 18:36 - loss: 0.0137 - accuracy: 1.000 320/3567 [=>............................] - ETA: 18:27 - loss: 0.0137 - accuracy: 1.000 336/3567 [=>............................] - ETA: 18:19 - loss: 0.0136 - accuracy: 1.000 352/3567 [=>............................] - ETA: 18:11 - loss: 0.0136 - accuracy: 1.000 368/3567 [==>...........................] - ETA: 18:03 - loss: 0.0136 - accuracy: 1.000 384/3567 [==>...........................] - 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ETA: 16:38 - loss: 0.0209 - accuracy: 0.998 592/3567 [===>..........................] - ETA: 16:31 - loss: 0.0207 - accuracy: 0.998 608/3567 [====>.........................] - ETA: 16:25 - loss: 0.0205 - accuracy: 0.998 624/3567 [====>.........................] - ETA: 16:19 - loss: 0.0204 - accuracy: 0.998 640/3567 [====>.........................] - ETA: 16:13 - loss: 0.0202 - accuracy: 0.998 656/3567 [====>.........................] - ETA: 16:07 - loss: 0.0200 - accuracy: 0.998 672/3567 [====>.........................] - ETA: 16:01 - loss: 0.0199 - accuracy: 0.998 688/3567 [====>.........................] - ETA: 15:55 - loss: 0.0197 - accuracy: 0.998 704/3567 [====>.........................] - ETA: 15:49 - loss: 0.0196 - accuracy: 0.998 720/3567 [=====>........................] - ETA: 15:43 - loss: 0.0254 - accuracy: 0.997 736/3567 [=====>........................] - ETA: 15:37 - loss: 0.0251 - accuracy: 0.997 752/3567 [=====>........................] - ETA: 15:31 - loss: 0.0249 - accuracy: 0.997 768/3567 [=====>........................] - ETA: 15:25 - loss: 0.0246 - accuracy: 0.997 784/3567 [=====>........................] - ETA: 15:19 - loss: 0.0244 - accuracy: 0.997 800/3567 [=====>........................] - ETA: 15:14 - loss: 0.0242 - accuracy: 0.997 816/3567 [=====>........................] - ETA: 15:08 - loss: 0.0240 - accuracy: 0.997 832/3567 [=====>........................] - ETA: 15:02 - loss: 0.0238 - accuracy: 0.997 848/3567 [======>.......................] - ETA: 14:57 - loss: 0.0236 - accuracy: 0.997 864/3567 [======>.......................] - ETA: 14:51 - loss: 0.0235 - accuracy: 0.997 880/3567 [======>.......................] - ETA: 14:45 - loss: 0.0233 - accuracy: 0.997 896/3567 [======>.......................] - ETA: 14:40 - loss: 0.0231 - accuracy: 0.997 912/3567 [======>.......................] - ETA: 14:34 - loss: 0.0230 - accuracy: 0.997 928/3567 [======>.......................] - ETA: 14:28 - loss: 0.0228 - accuracy: 0.997 944/3567 [======>.......................] - ETA: 14:23 - loss: 0.0227 - accuracy: 0.997 960/3567 [=======>......................] - ETA: 14:17 - loss: 0.0226 - accuracy: 0.997 976/3567 [=======>......................] - ETA: 14:12 - loss: 0.0224 - accuracy: 0.998 992/3567 [=======>......................] - ETA: 14:06 - loss: 0.0223 - accuracy: 0.9981008/3567 [=======>......................] - ETA: 14:01 - loss: 0.0227 - accuracy: 0.9981024/3567 [=======>......................] - ETA: 13:55 - loss: 0.0226 - accuracy: 0.9981040/3567 [=======>......................] - ETA: 13:50 - loss: 0.0225 - accuracy: 0.9981056/3567 [=======>......................] - ETA: 13:44 - loss: 0.0223 - accuracy: 0.9981072/3567 [========>.....................] - ETA: 13:39 - loss: 0.0222 - accuracy: 0.9981088/3567 [========>.....................] - ETA: 13:33 - loss: 0.0221 - accuracy: 0.9981104/3567 [========>.....................] - ETA: 13:28 - loss: 0.0219 - accuracy: 0.9981120/3567 [========>.....................] - ETA: 13:22 - loss: 0.0218 - accuracy: 0.9981136/3567 [========>.....................] - ETA: 13:17 - loss: 0.0217 - accuracy: 0.9981152/3567 [========>.....................] - ETA: 13:11 - loss: 0.0216 - accuracy: 0.9981168/3567 [========>.....................] - ETA: 13:06 - loss: 0.0215 - accuracy: 0.9981184/3567 [========>.....................] - ETA: 13:01 - loss: 0.0214 - accuracy: 0.9981200/3567 [=========>....................] - ETA: 12:55 - loss: 0.0225 - accuracy: 0.9971216/3567 [=========>....................] - ETA: 12:50 - loss: 0.0224 - accuracy: 0.9971232/3567 [=========>....................] - ETA: 12:44 - loss: 0.0223 - accuracy: 0.9971248/3567 [=========>....................] - ETA: 12:39 - loss: 0.0222 - accuracy: 0.9971264/3567 [=========>....................] - ETA: 12:34 - loss: 0.0221 - accuracy: 0.9971280/3567 [=========>....................] - ETA: 12:28 - loss: 0.0220 - accuracy: 0.9971296/3567 [=========>....................] - ETA: 12:23 - loss: 0.0219 - accuracy: 0.9971312/3567 [==========>...................] - ETA: 12:17 - loss: 0.0250 - accuracy: 0.9971328/3567 [==========>...................] - ETA: 12:12 - loss: 0.0249 - accuracy: 0.9971344/3567 [==========>...................] - ETA: 12:07 - loss: 0.0248 - accuracy: 0.9971360/3567 [==========>...................] - ETA: 12:02 - loss: 0.0246 - accuracy: 0.9971376/3567 [==========>...................] - ETA: 11:56 - loss: 0.0245 - accuracy: 0.9971392/3567 [==========>...................] - ETA: 11:51 - loss: 0.0244 - accuracy: 0.9971408/3567 [==========>...................] - ETA: 11:45 - loss: 0.0243 - accuracy: 0.9971424/3567 [==========>...................] - ETA: 11:40 - loss: 0.0242 - accuracy: 0.9971440/3567 [===========>..................] - ETA: 11:35 - loss: 0.0241 - accuracy: 0.9971456/3567 [===========>..................] - ETA: 11:29 - loss: 0.0241 - accuracy: 0.9971472/3567 [===========>..................] - ETA: 11:24 - loss: 0.0240 - accuracy: 0.9971488/3567 [===========>..................] - ETA: 11:19 - loss: 0.0239 - accuracy: 0.9971504/3567 [===========>..................] - ETA: 11:13 - loss: 0.0238 - accuracy: 0.9971520/3567 [===========>..................] - ETA: 11:08 - loss: 0.0237 - accuracy: 0.9971536/3567 [===========>..................] - ETA: 11:03 - loss: 0.0236 - accuracy: 0.9971552/3567 [============>.................] - ETA: 10:58 - loss: 0.0235 - accuracy: 0.9971568/3567 [============>.................] - ETA: 10:52 - loss: 0.0236 - accuracy: 0.9971584/3567 [============>.................] - ETA: 10:47 - loss: 0.0235 - accuracy: 0.9971600/3567 [============>.................] - ETA: 10:42 - loss: 0.0234 - accuracy: 0.9971616/3567 [============>.................] - ETA: 10:36 - loss: 0.0233 - accuracy: 0.9971632/3567 [============>.................] - ETA: 10:31 - loss: 0.0258 - accuracy: 0.9961648/3567 [============>.................] - ETA: 10:26 - loss: 0.0256 - accuracy: 0.9971664/3567 [============>.................] - ETA: 10:20 - loss: 0.0255 - accuracy: 0.9971680/3567 [=============>................] - ETA: 10:15 - loss: 0.0254 - accuracy: 0.9971696/3567 [=============>................] - ETA: 10:10 - loss: 0.0253 - accuracy: 0.9971712/3567 [=============>................] - ETA: 10:05 - loss: 0.0252 - accuracy: 0.9971728/3567 [=============>................] - ETA: 9:59 - loss: 0.0251 - accuracy: 0.99713567/3567 [==============================] - 1196s 335ms/step - loss: 0.0229 - accuracy: 0.9978 - val_loss: 0.0478 - val_accuracy: 0.9924
  141. Epoch 2/3
  142. 16/3567 [..............................] - ETA: 19:05 - loss: 0.0136 - accuracy: 1.000 32/3567 [..............................] - ETA: 19:02 - loss: 0.0135 - accuracy: 1.000 48/3567 [..............................] - ETA: 18:55 - loss: 0.0135 - accuracy: 1.000 64/3567 [..............................] - ETA: 18:50 - loss: 0.0137 - accuracy: 1.000 80/3567 [..............................] - ETA: 18:44 - loss: 0.0136 - accuracy: 1.000 96/3567 [..............................] - ETA: 18:40 - loss: 0.0136 - accuracy: 1.000 112/3567 [..............................] - ETA: 18:34 - loss: 0.0136 - accuracy: 1.000 128/3567 [>.............................] - ETA: 18:29 - loss: 0.0136 - accuracy: 1.000 144/3567 [>.............................] - ETA: 18:23 - loss: 0.0136 - accuracy: 1.000 160/3567 [>.............................] - ETA: 18:18 - loss: 0.0137 - accuracy: 1.000 176/3567 [>.............................] - ETA: 18:13 - loss: 0.0137 - accuracy: 1.000 192/3567 [>.............................] - ETA: 18:08 - loss: 0.0137 - accuracy: 1.000 208/3567 [>.............................] - ETA: 18:02 - loss: 0.0138 - accuracy: 1.000 224/3567 [>.............................] - ETA: 17:57 - loss: 0.0138 - accuracy: 1.000 240/3567 [=>............................] - ETA: 17:52 - loss: 0.0138 - accuracy: 1.000 256/3567 [=>............................] - ETA: 17:47 - loss: 0.0139 - accuracy: 1.000 272/3567 [=>............................] - ETA: 17:42 - loss: 0.0139 - accuracy: 1.000 288/3567 [=>............................] - ETA: 17:36 - loss: 0.0138 - accuracy: 1.000 304/3567 [=>............................] - ETA: 17:31 - loss: 0.0138 - accuracy: 1.000 320/3567 [=>............................] - ETA: 17:26 - loss: 0.0140 - accuracy: 1.000 336/3567 [=>............................] - ETA: 17:21 - loss: 0.0139 - accuracy: 1.000 352/3567 [=>............................] - ETA: 17:16 - loss: 0.0139 - accuracy: 1.000 368/3567 [==>...........................] - ETA: 17:11 - loss: 0.0139 - accuracy: 1.000 384/3567 [==>...........................] - ETA: 17:06 - loss: 0.0140 - accuracy: 1.000 400/3567 [==>...........................] - ETA: 17:00 - loss: 0.0139 - accuracy: 1.000 416/3567 [==>...........................] - ETA: 16:55 - loss: 0.0139 - accuracy: 1.000 432/3567 [==>...........................] - ETA: 16:50 - loss: 0.0139 - accuracy: 1.000 448/3567 [==>...........................] - ETA: 16:45 - loss: 0.0139 - accuracy: 1.000 464/3567 [==>...........................] - ETA: 16:40 - loss: 0.0139 - accuracy: 1.000 480/3567 [===>..........................] - ETA: 16:35 - loss: 0.0140 - accuracy: 1.000 496/3567 [===>..........................] - ETA: 16:29 - loss: 0.0140 - accuracy: 1.000 512/3567 [===>..........................] - ETA: 16:24 - loss: 0.0140 - accuracy: 1.000 528/3567 [===>..........................] - ETA: 16:19 - loss: 0.0139 - accuracy: 1.000 544/3567 [===>..........................] - ETA: 16:14 - loss: 0.0139 - accuracy: 1.000 560/3567 [===>..........................] - ETA: 16:09 - loss: 0.0139 - accuracy: 1.000 576/3567 [===>..........................] - ETA: 16:03 - loss: 0.0139 - accuracy: 1.000 592/3567 [===>..........................] - ETA: 15:58 - loss: 0.0139 - accuracy: 1.000 608/3567 [====>.........................] - ETA: 15:53 - loss: 0.0139 - accuracy: 1.000 624/3567 [====>.........................] - ETA: 15:48 - loss: 0.0139 - accuracy: 1.000 640/3567 [====>.........................] - ETA: 15:43 - loss: 0.0139 - accuracy: 1.000 656/3567 [====>.........................] - ETA: 15:38 - loss: 0.0139 - accuracy: 1.000 672/3567 [====>.........................] - ETA: 15:33 - loss: 0.0139 - accuracy: 1.000 688/3567 [====>.........................] - ETA: 15:27 - loss: 0.0139 - accuracy: 1.000 704/3567 [====>.........................] - ETA: 15:22 - loss: 0.0139 - accuracy: 1.000 720/3567 [=====>........................] - ETA: 15:17 - loss: 0.0139 - accuracy: 1.000 736/3567 [=====>........................] - ETA: 15:12 - loss: 0.0139 - accuracy: 1.000 752/3567 [=====>........................] - ETA: 15:07 - loss: 0.0139 - accuracy: 1.000 768/3567 [=====>........................] - ETA: 15:02 - loss: 0.0139 - accuracy: 1.000 784/3567 [=====>........................] - ETA: 14:57 - loss: 0.0139 - accuracy: 1.000 800/3567 [=====>........................] - ETA: 14:51 - loss: 0.0139 - accuracy: 1.000 816/3567 [=====>........................] - ETA: 14:46 - loss: 0.0138 - accuracy: 1.000 832/3567 [=====>........................] - ETA: 14:41 - loss: 0.0139 - accuracy: 1.000 848/3567 [======>.......................] - ETA: 14:36 - loss: 0.0139 - accuracy: 1.000 864/3567 [======>.......................] - ETA: 14:31 - loss: 0.0188 - accuracy: 0.998 880/3567 [======>.......................] - ETA: 14:26 - loss: 0.0187 - accuracy: 0.998 896/3567 [======>.......................] - ETA: 14:20 - loss: 0.0186 - accuracy: 0.998 912/3567 [======>.......................] - ETA: 14:15 - loss: 0.0185 - accuracy: 0.998 928/3567 [======>.......................] - ETA: 14:10 - loss: 0.0185 - accuracy: 0.998 944/3567 [======>.......................] - ETA: 14:05 - loss: 0.0184 - accuracy: 0.998 960/3567 [=======>......................] - ETA: 14:00 - loss: 0.0183 - accuracy: 0.999 976/3567 [=======>......................] - ETA: 13:54 - loss: 0.0182 - accuracy: 0.999 992/3567 [=======>......................] - ETA: 13:49 - loss: 0.0182 - accuracy: 0.9991008/3567 [=======>......................] - ETA: 13:44 - loss: 0.0181 - accuracy: 0.9991024/3567 [=======>......................] - ETA: 13:39 - loss: 0.0221 - accuracy: 0.9981040/3567 [=======>......................] - ETA: 13:34 - loss: 0.0220 - accuracy: 0.9981056/3567 [=======>......................] - ETA: 13:29 - loss: 0.0218 - accuracy: 0.9981072/3567 [========>.....................] - ETA: 13:24 - loss: 0.0217 - accuracy: 0.9981088/3567 [========>.....................] - ETA: 13:18 - loss: 0.0216 - accuracy: 0.9981104/3567 [========>.....................] - ETA: 13:13 - loss: 0.0253 - accuracy: 0.9971120/3567 [========>.....................] - ETA: 13:08 - loss: 0.0252 - accuracy: 0.9971136/3567 [========>.....................] - ETA: 13:03 - loss: 0.0250 - accuracy: 0.9971152/3567 [========>.....................] - ETA: 12:58 - loss: 0.0250 - accuracy: 0.9971168/3567 [========>.....................] - ETA: 12:53 - loss: 0.0249 - accuracy: 0.9971184/3567 [========>.....................] - ETA: 12:47 - loss: 0.0248 - accuracy: 0.9971200/3567 [=========>....................] - ETA: 12:42 - loss: 0.0246 - accuracy: 0.9971216/3567 [=========>....................] - ETA: 12:37 - loss: 0.0245 - accuracy: 0.9971232/3567 [=========>....................] - ETA: 12:32 - loss: 0.0243 - accuracy: 0.9971248/3567 [=========>....................] - ETA: 12:27 - loss: 0.0242 - accuracy: 0.9971264/3567 [=========>....................] - ETA: 12:22 - loss: 0.0240 - accuracy: 0.9971280/3567 [=========>....................] - ETA: 12:16 - loss: 0.0239 - accuracy: 0.9971296/3567 [=========>....................] - ETA: 12:11 - loss: 0.0238 - accuracy: 0.9971312/3567 [==========>...................] - ETA: 12:06 - loss: 0.0237 - accuracy: 0.9971328/3567 [==========>...................] - ETA: 12:01 - loss: 0.0235 - accuracy: 0.9971344/3567 [==========>...................] - ETA: 11:56 - loss: 0.0234 - accuracy: 0.9971360/3567 [==========>...................] - ETA: 11:51 - loss: 0.0234 - accuracy: 0.9971376/3567 [==========>...................] - ETA: 11:46 - loss: 0.0233 - accuracy: 0.9971392/3567 [==========>...................] - ETA: 11:40 - loss: 0.0232 - accuracy: 0.9971408/3567 [==========>...................] - ETA: 11:35 - loss: 0.0231 - accuracy: 0.9971424/3567 [==========>...................] - ETA: 11:30 - loss: 0.0230 - accuracy: 0.9971440/3567 [===========>..................] - ETA: 11:25 - loss: 0.0229 - accuracy: 0.9971456/3567 [===========>..................] - ETA: 11:20 - loss: 0.0228 - accuracy: 0.9971472/3567 [===========>..................] - ETA: 11:15 - loss: 0.0256 - accuracy: 0.9971488/3567 [===========>..................] - ETA: 11:09 - loss: 0.0255 - accuracy: 0.9971504/3567 [===========>..................] - ETA: 11:04 - loss: 0.0254 - accuracy: 0.9971520/3567 [===========>..................] - ETA: 10:59 - loss: 0.0252 - accuracy: 0.9971536/3567 [===========>..................] - ETA: 10:54 - loss: 0.0251 - accuracy: 0.9971552/3567 [============>.................] - ETA: 10:49 - loss: 0.0250 - accuracy: 0.9971568/3567 [============>.................] - ETA: 10:44 - loss: 0.0249 - accuracy: 0.9971584/3567 [============>.................] - ETA: 10:39 - loss: 0.0248 - accuracy: 0.9971600/3567 [============>.................] - ETA: 10:33 - loss: 0.0247 - accuracy: 0.9971616/3567 [============>.................] - ETA: 10:28 - loss: 0.0245 - accuracy: 0.9971632/3567 [============>.................] - ETA: 10:23 - loss: 0.0244 - accuracy: 0.9971648/3567 [============>.................] - ETA: 10:18 - loss: 0.0243 - accuracy: 0.9971664/3567 [============>.................] - ETA: 10:13 - loss: 0.0242 - accuracy: 0.9971680/3567 [=============>................] - ETA: 10:08 - loss: 0.0241 - accuracy: 0.9971696/3567 [=============>................] - ETA: 10:02 - loss: 0.0240 - accuracy: 0.9971712/3567 [=============>................] - ETA: 9:57 - loss: 0.0239 - accuracy: 0.99773567/3567 [==============================] - 1189s 333ms/step - loss: 0.0223 - accuracy: 0.9980 - val_loss: 0.0487 - val_accuracy: 0.9899
  143. Epoch 3/3
  144. 16/3567 [..............................] - ETA: 19:05 - loss: 0.0149 - accuracy: 1.000 32/3567 [..............................] - ETA: 18:58 - loss: 0.0143 - accuracy: 1.000 48/3567 [..............................] - ETA: 18:53 - loss: 0.0141 - accuracy: 1.000 64/3567 [..............................] - ETA: 18:48 - loss: 0.0140 - accuracy: 1.000 80/3567 [..............................] - ETA: 18:43 - loss: 0.0141 - accuracy: 1.000 96/3567 [..............................] - ETA: 18:38 - loss: 0.0140 - accuracy: 1.000 112/3567 [..............................] - ETA: 18:33 - loss: 0.0140 - accuracy: 1.000 128/3567 [>.............................] - ETA: 18:28 - loss: 0.0140 - accuracy: 1.000 144/3567 [>.............................] - ETA: 18:23 - loss: 0.0140 - accuracy: 1.000 160/3567 [>.............................] - ETA: 18:18 - loss: 0.0140 - accuracy: 1.000 176/3567 [>.............................] - ETA: 18:13 - loss: 0.0139 - accuracy: 1.000 192/3567 [>.............................] - ETA: 18:08 - loss: 0.0142 - accuracy: 1.000 208/3567 [>.............................] - ETA: 18:03 - loss: 0.0141 - accuracy: 1.000 224/3567 [>.............................] - ETA: 17:58 - loss: 0.0142 - accuracy: 1.000 240/3567 [=>............................] - ETA: 17:53 - loss: 0.0141 - accuracy: 1.000 256/3567 [=>............................] - ETA: 17:48 - loss: 0.0146 - accuracy: 1.000 272/3567 [=>............................] - ETA: 17:42 - loss: 0.0146 - accuracy: 1.000 288/3567 [=>............................] - ETA: 17:37 - loss: 0.0145 - accuracy: 1.000 304/3567 [=>............................] - ETA: 17:32 - loss: 0.0145 - accuracy: 1.000 320/3567 [=>............................] - ETA: 17:27 - loss: 0.0145 - accuracy: 1.000 336/3567 [=>............................] - ETA: 17:22 - loss: 0.0145 - accuracy: 1.000 352/3567 [=>............................] - ETA: 17:17 - loss: 0.0144 - accuracy: 1.000 368/3567 [==>...........................] - ETA: 17:12 - loss: 0.0144 - accuracy: 1.000 384/3567 [==>...........................] - ETA: 17:06 - loss: 0.0144 - accuracy: 1.000 400/3567 [==>...........................] - ETA: 17:01 - loss: 0.0143 - accuracy: 1.000 416/3567 [==>...........................] - ETA: 16:56 - loss: 0.0143 - accuracy: 1.000 432/3567 [==>...........................] - ETA: 16:51 - loss: 0.0143 - accuracy: 1.000 448/3567 [==>...........................] - ETA: 16:46 - loss: 0.0143 - accuracy: 1.000 464/3567 [==>...........................] - ETA: 16:40 - loss: 0.0143 - accuracy: 1.000 480/3567 [===>..........................] - ETA: 16:35 - loss: 0.0143 - accuracy: 1.000 496/3567 [===>..........................] - ETA: 16:30 - loss: 0.0143 - accuracy: 1.000 512/3567 [===>..........................] - ETA: 16:25 - loss: 0.0143 - accuracy: 1.000 528/3567 [===>..........................] - ETA: 16:20 - loss: 0.0144 - accuracy: 1.000 544/3567 [===>..........................] - ETA: 16:14 - loss: 0.0143 - accuracy: 1.000 560/3567 [===>..........................] - ETA: 16:09 - loss: 0.0143 - accuracy: 1.000 576/3567 [===>..........................] - 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ETA: 12:59 - loss: 0.0218 - accuracy: 0.9981168/3567 [========>.....................] - ETA: 12:54 - loss: 0.0217 - accuracy: 0.9981184/3567 [========>.....................] - ETA: 12:48 - loss: 0.0216 - accuracy: 0.9981200/3567 [=========>....................] - ETA: 12:43 - loss: 0.0215 - accuracy: 0.9981216/3567 [=========>....................] - ETA: 12:38 - loss: 0.0214 - accuracy: 0.9981232/3567 [=========>....................] - ETA: 12:33 - loss: 0.0213 - accuracy: 0.9981248/3567 [=========>....................] - ETA: 12:28 - loss: 0.0212 - accuracy: 0.9981264/3567 [=========>....................] - ETA: 12:23 - loss: 0.0211 - accuracy: 0.9981280/3567 [=========>....................] - ETA: 12:17 - loss: 0.0210 - accuracy: 0.9981296/3567 [=========>....................] - ETA: 12:12 - loss: 0.0209 - accuracy: 0.9981312/3567 [==========>...................] - ETA: 12:07 - loss: 0.0208 - accuracy: 0.9981328/3567 [==========>...................] - ETA: 12:02 - loss: 0.0208 - accuracy: 0.9981344/3567 [==========>...................] - ETA: 11:57 - loss: 0.0207 - accuracy: 0.9981360/3567 [==========>...................] - ETA: 11:52 - loss: 0.0206 - accuracy: 0.9981376/3567 [==========>...................] - ETA: 11:46 - loss: 0.0205 - accuracy: 0.9981392/3567 [==========>...................] - ETA: 11:41 - loss: 0.0204 - accuracy: 0.9981408/3567 [==========>...................] - ETA: 11:36 - loss: 0.0204 - accuracy: 0.9981424/3567 [==========>...................] - ETA: 11:31 - loss: 0.0203 - accuracy: 0.9981440/3567 [===========>..................] - ETA: 11:26 - loss: 0.0202 - accuracy: 0.9981456/3567 [===========>..................] - ETA: 11:21 - loss: 0.0230 - accuracy: 0.9971472/3567 [===========>..................] - ETA: 11:15 - loss: 0.0229 - accuracy: 0.9981488/3567 [===========>..................] - ETA: 11:10 - loss: 0.0228 - accuracy: 0.9981504/3567 [===========>..................] - ETA: 11:05 - loss: 0.0227 - accuracy: 0.9981520/3567 [===========>..................] - ETA: 11:00 - loss: 0.0227 - accuracy: 0.9981536/3567 [===========>..................] - ETA: 10:55 - loss: 0.0226 - accuracy: 0.9981552/3567 [============>.................] - ETA: 10:50 - loss: 0.0225 - accuracy: 0.9981568/3567 [============>.................] - ETA: 10:44 - loss: 0.0224 - accuracy: 0.9981584/3567 [============>.................] - ETA: 10:39 - loss: 0.0223 - accuracy: 0.9981600/3567 [============>.................] - ETA: 10:34 - loss: 0.0223 - accuracy: 0.9981616/3567 [============>.................] - ETA: 10:29 - loss: 0.0222 - accuracy: 0.9981632/3567 [============>.................] - ETA: 10:24 - loss: 0.0221 - accuracy: 0.9981648/3567 [============>.................] - ETA: 10:19 - loss: 0.0246 - accuracy: 0.9971664/3567 [============>.................] - ETA: 10:13 - loss: 0.0248 - accuracy: 0.9971680/3567 [=============>................] - ETA: 10:08 - loss: 0.0247 - accuracy: 0.9971696/3567 [=============>................] - ETA: 10:03 - loss: 0.0246 - accuracy: 0.9971712/3567 [=============>................] - ETA: 9:58 - loss: 0.0245 - accuracy: 0.99773567/3567 [==============================] - 1190s 334ms/step - loss: 0.0227 - accuracy: 0.9980 - val_loss: 0.0471 - val_accuracy: 0.9924
  145.  
  146. Train Feature Extraction...
  147. Train feature extraction finished in 391.542492 seconds.
  148. Test feature extraction finished in 121.536859 seconds.
  149. precision recall f1-score support
  150.  
  151. 0 1.00 1.00 1.00 418
  152. 1 0.99 1.00 1.00 376
  153.  
  154. accuracy 1.00 794
  155. macro avg 1.00 1.00 1.00 794
  156. weighted avg 1.00 1.00 1.00 794
  157.  
  158. Acc: 0.9962216624685138 794 791.0
  159. Train Feature Extraction...
  160. Train feature extraction finished in 458.240229 seconds.
  161. Test feature extraction finished in 118.189247 seconds.
  162. precision recall f1-score support
  163.  
  164. 0 0.86 1.00 0.92 418
  165. 1 0.99 0.82 0.90 375
  166.  
  167. accuracy 0.91 793
  168. macro avg 0.93 0.91 0.91 793
  169. weighted avg 0.92 0.91 0.91 793
  170.  
  171. Acc: 0.914249684741488 793 725.0
  172. Train Feature Extraction...
  173. Train feature extraction finished in 461.826579 seconds.
  174. Test feature extraction finished in 122.012805 seconds.
  175. precision recall f1-score support
  176.  
  177. 0 1.00 1.00 1.00 418
  178. 1 1.00 0.99 1.00 375
  179.  
  180. accuracy 1.00 793
  181. macro avg 1.00 1.00 1.00 793
  182. weighted avg 1.00 1.00 1.00 793
  183.  
  184. Acc: 0.9974779319041615 793 791.0
  185. Train Feature Extraction...
  186. Train feature extraction finished in 481.979259 seconds.
  187. Test feature extraction finished in 123.341541 seconds.
  188. precision recall f1-score support
  189.  
  190. 0 0.99 1.00 0.99 417
  191. 1 0.99 0.99 0.99 375
  192.  
  193. accuracy 0.99 792
  194. macro avg 0.99 0.99 0.99 792
  195. weighted avg 0.99 0.99 0.99 792
  196.  
  197. Acc: 0.9936868686868687 792 787.0
  198. Train Feature Extraction...
  199. Train feature extraction finished in 487.685289 seconds.
  200. Test feature extraction finished in 119.146486 seconds.
  201. precision recall f1-score support
  202.  
  203. 0 1.00 1.00 1.00 417
  204. 1 0.99 1.00 1.00 375
  205.  
  206. accuracy 1.00 792
  207. macro avg 1.00 1.00 1.00 792
  208. weighted avg 1.00 1.00 1.00 792
  209.  
  210. Acc: 0.9962121212121212 792 789.0
  211. End: 3883.0 3964 0.9795660948536832
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