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lamiastella

modified loocv tl res

Nov 25th, 2018
275
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  1. Using sample 48 as test data
  2. Resetting model
  3. Epoch 0/9
  4. ----------
  5. Loss: 0.7175 Acc: 0.5000
  6. Epoch 1/9
  7. ----------
  8. Loss: 0.7165 Acc: 0.5000
  9. Epoch 2/9
  10. ----------
  11. Loss: 0.7154 Acc: 0.5000
  12. Epoch 3/9
  13. ----------
  14. Loss: 0.7156 Acc: 0.5000
  15. Epoch 4/9
  16. ----------
  17. Loss: 0.7180 Acc: 0.5000
  18. Epoch 5/9
  19. ----------
  20. Loss: 0.7164 Acc: 0.5000
  21. Epoch 6/9
  22. ----------
  23. Loss: 0.7166 Acc: 0.5000
  24. Epoch 7/9
  25. ----------
  26. Loss: 0.7163 Acc: 0.5000
  27. Epoch 8/9
  28. ----------
  29. Loss: 0.7172 Acc: 0.5000
  30. Epoch 9/9
  31. ----------
  32. Loss: 0.7165 Acc: 0.5000
  33. Training complete in 0m 21s
  34. <class 'torch.Tensor'>
  35. torch.Size([1, 3, 224, 224])
  36. Using sample 49 as test data
  37. Resetting model
  38. Epoch 0/9
  39. ----------
  40. Loss: 0.7190 Acc: 0.5000
  41. Epoch 1/9
  42. ----------
  43. Loss: 0.7185 Acc: 0.5000
  44. Epoch 2/9
  45. ----------
  46. Loss: 0.7134 Acc: 0.5000
  47. Epoch 3/9
  48. ----------
  49. Loss: 0.7175 Acc: 0.5000
  50. Epoch 4/9
  51. ----------
  52. Loss: 0.7204 Acc: 0.5000
  53. Epoch 5/9
  54. ----------
  55. Loss: 0.7188 Acc: 0.5000
  56. Epoch 6/9
  57. ----------
  58. Loss: 0.7172 Acc: 0.5000
  59. Epoch 7/9
  60. ----------
  61. Loss: 0.7183 Acc: 0.5000
  62. Epoch 8/9
  63. ----------
  64. Loss: 0.7139 Acc: 0.5000
  65. Epoch 9/9
  66. ----------
  67. Loss: 0.7160 Acc: 0.5000
  68. Training complete in 0m 22s
  69. <class 'torch.Tensor'>
  70. torch.Size([1, 3, 224, 224])
  71. loocv preds: [tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0'), tensor([0], device='cuda:0')]
  72. loocv targets: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
  73. acc score: 0.5
  74. confusion matrix:
  75. [[25 0]
  76. [25 0]]
  77. confidence score for each image: tensor([[0.7054, 0.2946]], device='cuda:0', grad_fn=<SoftmaxBackward>)
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