May 3, 2025

1. 00:13

   epoch: 98, valid_loss: 0.0128, accuracy: 1.0

   leoschneider
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2. 00:13

   epoch: 97, valid_loss: 0.013, accuracy: 1.0

   leoschneider
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3. 00:13

   epoch: 96, valid_loss: 0.0132, accuracy: 1.0

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4. 00:12

   epoch: 94, valid_loss: 0.0137, accuracy: 1.0

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5. 00:12

   epoch: 93, valid_loss: 0.0141, accuracy: 1.0

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6. 00:12

   epoch: 90, valid_loss: 0.0145, accuracy: 1.0

   leoschneider
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   5082a5b
7. 00:12

   epoch: 89, valid_loss: 0.015, accuracy: 1.0

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8. 00:12

   epoch: 88, valid_loss: 0.0153, accuracy: 1.0

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9. 00:12

   epoch: 87, valid_loss: 0.0155, accuracy: 1.0

   leoschneider
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10. 00:12

    epoch: 86, valid_loss: 0.0157, accuracy: 1.0

    leoschneider
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11. 00:12

    epoch: 85, valid_loss: 0.016, accuracy: 1.0

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    c614e03
12. 00:11

    epoch: 84, valid_loss: 0.0161, accuracy: 1.0

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13. 00:11

    epoch: 83, valid_loss: 0.0166, accuracy: 1.0

    leoschneider
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14. 00:11

    epoch: 81, valid_loss: 0.017, accuracy: 1.0

    leoschneider
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15. 00:11

    epoch: 80, valid_loss: 0.0173, accuracy: 1.0

    leoschneider
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16. 00:11

    epoch: 79, valid_loss: 0.0178, accuracy: 1.0

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17. 00:11

    epoch: 76, valid_loss: 0.0185, accuracy: 1.0

    leoschneider
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18. 00:11

    epoch: 75, valid_loss: 0.019, accuracy: 1.0

    leoschneider
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19. 00:11

    epoch: 74, valid_loss: 0.0195, accuracy: 1.0

    leoschneider
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    c4c6c04
20. 00:10

    epoch: 73, valid_loss: 0.0197, accuracy: 1.0

    leoschneider
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21. 00:10

    epoch: 72, valid_loss: 0.0207, accuracy: 1.0

    leoschneider
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22. 00:10

    epoch: 71, valid_loss: 0.021, accuracy: 1.0

    leoschneider
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23. 00:10

    epoch: 70, valid_loss: 0.0218, accuracy: 1.0

    leoschneider
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24. 00:10

    epoch: 69, valid_loss: 0.0221, accuracy: 1.0

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25. 00:10

    epoch: 67, valid_loss: 0.0233, accuracy: 1.0

    leoschneider
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26. 00:10

    epoch: 65, valid_loss: 0.0242, accuracy: 1.0

    leoschneider
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27. 00:09

    epoch: 63, valid_loss: 0.0251, accuracy: 1.0

    leoschneider
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28. 00:09

    epoch: 61, valid_loss: 0.0264, accuracy: 1.0

    leoschneider
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29. 00:09

    epoch: 60, valid_loss: 0.0274, accuracy: 1.0

    leoschneider
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30. 00:09

    epoch: 59, valid_loss: 0.0282, accuracy: 1.0

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31. 00:09

    epoch: 58, valid_loss: 0.0287, accuracy: 1.0

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32. 00:09

    epoch: 56, valid_loss: 0.0305, accuracy: 1.0

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33. 00:09

    epoch: 55, valid_loss: 0.0312, accuracy: 1.0

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34. 00:09

    epoch: 54, valid_loss: 0.0317, accuracy: 1.0

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35. 00:09

    epoch: 53, valid_loss: 0.033, accuracy: 1.0

    leoschneider
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36. 00:08

    epoch: 51, valid_loss: 0.0357, accuracy: 1.0

    leoschneider
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    8b9ac17
37. 00:08

    epoch: 49, valid_loss: 0.0378, accuracy: 1.0

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38. 00:08

    epoch: 48, valid_loss: 0.0395, accuracy: 1.0

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39. 00:08

    epoch: 47, valid_loss: 0.0406, accuracy: 1.0

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40. 00:08

    epoch: 45, valid_loss: 0.0444, accuracy: 1.0

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41. 00:08

    epoch: 44, valid_loss: 0.0445, accuracy: 1.0

    leoschneider
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42. 00:08

    epoch: 43, valid_loss: 0.0485, accuracy: 1.0

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43. 00:08

    epoch: 42, valid_loss: 0.0511, accuracy: 1.0

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44. 00:08

    epoch: 41, valid_loss: 0.0523, accuracy: 1.0

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    32691ee
45. 00:07

    epoch: 40, valid_loss: 0.0556, accuracy: 1.0

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    990d944
46. 00:07

    epoch: 38, valid_loss: 0.0572, accuracy: 1.0

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47. 00:07

    epoch: 36, valid_loss: 0.0623, accuracy: 1.0

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48. 00:07

    epoch: 35, valid_loss: 0.066, accuracy: 1.0

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    82d7d28
49. 00:07

    epoch: 34, valid_loss: 0.0704, accuracy: 1.0

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50. 00:07

    epoch: 33, valid_loss: 0.0738, accuracy: 1.0

    leoschneider
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