log20epochs.txt

==== RESTART: C:\CarsModels_Resnet_Pytorch\TrainCarsModels_Resnet_Pytorch.py ===

Warning (from warnings module):
  File "C:\Users\Alfonso Blanco\.conda\envs\alfonso1\lib\site-packages\numpy\_distributor_init.py", line 30
    warnings.warn("loaded more than 1 DLL from .libs:"
UserWarning: loaded more than 1 DLL from .libs:
C:\Users\Alfonso Blanco\.conda\envs\alfonso1\lib\site-packages\numpy\.libs\libopenblas.FB5AE2TYXYH2IJRDKGDGQ3XBKLKTF43H.gfortran-win_amd64.dll
C:\Users\Alfonso Blanco\.conda\envs\alfonso1\lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll

Warning (from warnings module):
  File "C:\Users\Alfonso Blanco\AppData\Roaming\Python\Python39\site-packages\torchvision\models\_utils.py", line 208
    warnings.warn(
UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.

Warning (from warnings module):
  File "C:\Users\Alfonso Blanco\AppData\Roaming\Python\Python39\site-packages\torchvision\models\_utils.py", line 223
    warnings.warn(msg)
UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
No. epochs: 2,             Training Loss: 0.122             Valid Loss: 4.928             Valid Accuracy: 0.033
No. epochs: 3,             Training Loss: 0.183             Valid Loss: 4.046             Valid Accuracy: 0.118
No. epochs: 4,             Training Loss: 0.211             Valid Loss: 3.313             Valid Accuracy: 0.237
No. epochs: 5,             Training Loss: 0.19             Valid Loss: 2.726             Valid Accuracy: 0.353
No. epochs: 6,             Training Loss: 0.179             Valid Loss: 2.113             Valid Accuracy: 0.465
No. epochs: 7,             Training Loss: 0.146             Valid Loss: 1.873             Valid Accuracy: 0.517
No. epochs: 8,             Training Loss: 0.121             Valid Loss: 1.624             Valid Accuracy: 0.589
No. epochs: 9,             Training Loss: 0.098             Valid Loss: 1.453             Valid Accuracy: 0.621
No. epochs: 10,             Training Loss: 0.081             Valid Loss: 1.405             Valid Accuracy: 0.621
No. epochs: 11,             Training Loss: 0.062             Valid Loss: 1.061             Valid Accuracy: 0.726
No. epochs: 12,             Training Loss: 0.066             Valid Loss: 1.03             Valid Accuracy: 0.733
No. epochs: 13,             Training Loss: 0.064             Valid Loss: 1.008             Valid Accuracy: 0.738
No. epochs: 14,             Training Loss: 0.067             Valid Loss: 1.016             Valid Accuracy: 0.731
No. epochs: 15,             Training Loss: 0.07             Valid Loss: 1.003             Valid Accuracy: 0.735
No. epochs: 16,             Training Loss: 0.076             Valid Loss: 1.005             Valid Accuracy: 0.741
No. epochs: 17,             Training Loss: 0.077             Valid Loss: 1.001             Valid Accuracy: 0.74
No. epochs: 18,             Training Loss: 0.083             Valid Loss: 0.997             Valid Accuracy: 0.74
No. epochs: 19,             Training Loss: 0.09             Valid Loss: 1.014             Valid Accuracy: 0.729
No. epochs: 20,             Training Loss: 0.092             Valid Loss: 0.985             Valid Accuracy: 0.745
Test accuracy of model: 69.89%
>>>