low accuracy value after pruning and retraining

[buttercutter]

When I use my own modified pytorch-pruning for python 3 with this fruits dataset , I have quite low accuracy value. May I know why ?

Besides, how do we use the output model_prunned ? Do we have to write our own pytorch code to test any random input images not in the test and training set ?

[phung@archlinux pytorch-pruning]$ ls -al test/
total 16
drwxr-xr-x 2 phung phung 4096 Oct 20 20:55 .
drwxr-xr-x 6 phung phung 4096 Oct 20 23:57 ..
lrwxrwxrwx 1 phung phung 96 Oct 20 20:54 Lemon -> /home/phung/Documents/Grive/Personal/Coursera/Machine_Learning/dataset/kaggle_fruits/Test/Lemon/
lrwxrwxrwx 1 phung phung 97 Oct 20 20:55 Orange -> /home/phung/Documents/Grive/Personal/Coursera/Machine_Learning/dataset/kaggle_fruits/Test/Orange/
[phung@archlinux pytorch-pruning]$ ls -al train/
total 16
drwxr-xr-x 2 phung phung 4096 Oct 20 20:54 .
drwxr-xr-x 6 phung phung 4096 Oct 20 23:57 ..
lrwxrwxrwx 1 phung phung 100 Oct 20 20:54 Lemon -> /home/phung/Documents/Grive/Personal/Coursera/Machine_Learning/dataset/kaggle_fruits/Training/Lemon/
lrwxrwxrwx 1 phung phung 100 Oct 20 20:53 Orange -> /home/phung/Documents/Grive/Personal/Coursera/Machine_Learning/dataset/kaggle_fruits/Training/Lemon/
[phung@archlinux pytorch-pruning]$ python finetune.py --train && python finetune.py --prune
/usr/lib/python3.7/site-packages/torchvision/transforms/transforms.py:187: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
warnings.warn("The use of the transforms.Scale transform is deprecated, " +
/usr/lib/python3.7/site-packages/torchvision/transforms/transforms.py:562: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
warnings.warn("The use of the transforms.RandomSizedCrop transform is deprecated, " +
Epoch: 0
Accuracy: 0.49382716049382713
Epoch: 1
Accuracy: 0.49382716049382713
Epoch: 2
Accuracy: 0.4876543209876543
Epoch: 3
Accuracy: 0.5339506172839507
Epoch: 4
Accuracy: 0.49382716049382713
Epoch: 5
Accuracy: 0.49382716049382713
Epoch: 6
Accuracy: 0.49382716049382713
Epoch: 7
Accuracy: 0.3765432098765432
Epoch: 8
Accuracy: 0.49382716049382713
Epoch: 9
Accuracy: 0.5
Epoch: 10
Accuracy: 0.49382716049382713
Epoch: 11
Accuracy: 0.49382716049382713
Epoch: 12
Accuracy: 0.49382716049382713
Epoch: 13
Accuracy: 0.24691358024691357
Epoch: 14
Accuracy: 0.3333333333333333
Epoch: 15
Accuracy: 0.4845679012345679
Epoch: 16
Accuracy: 0.5
Epoch: 17
Accuracy: 0.49382716049382713
Epoch: 18
Accuracy: 0.5030864197530864
Epoch: 19
Accuracy: 0.49382716049382713
Finished fine tuning.
/usr/lib/python3.7/site-packages/torchvision/transforms/transforms.py:187: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
warnings.warn("The use of the transforms.Scale transform is deprecated, " +
/usr/lib/python3.7/site-packages/torchvision/transforms/transforms.py:562: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
warnings.warn("The use of the transforms.RandomSizedCrop transform is deprecated, " +
Accuracy: 0.49382716049382713
Number of prunning iterations to reduce 67% filters 5
Ranking filters..
Layers that will be prunned {17: 62, 28: 117, 10: 19, 24: 57, 12: 20, 7: 8, 19: 57, 0: 9, 26: 60, 21: 72, 14: 22, 5: 6, 2: 3}
Prunning filters..
Filters prunned 87.87878787878788%
Accuracy: 0.49382716049382713
Fine tuning to recover from prunning iteration.
Epoch: 0
Accuracy: 0.5061728395061729
Epoch: 1
Accuracy: 0.4722222222222222
Epoch: 2
Accuracy: 0.5061728395061729
Epoch: 3
Accuracy: 0.49382716049382713
Epoch: 4
Accuracy: 0.558641975308642
Epoch: 5
Accuracy: 0.7469135802469136
Epoch: 6
Accuracy: 0.7314814814814815
Epoch: 7
Accuracy: 0.6234567901234568
Epoch: 8
Accuracy: 0.5061728395061729
Epoch: 9
Accuracy: 0.5401234567901234
Finished fine tuning.
Ranking filters..
Layers that will be prunned {28: 97, 21: 88, 19: 53, 17: 42, 24: 63, 26: 103, 12: 16, 5: 5, 10: 18, 14: 18, 7: 6, 2: 2, 0: 1}
Prunning filters..
Filters prunned 75.75757575757575%
Accuracy: 0.49382716049382713
Fine tuning to recover from prunning iteration.
Epoch: 0
Accuracy: 0.5061728395061729
Epoch: 1
Accuracy: 0.404320987654321
Epoch: 2
Accuracy: 0.24691358024691357
Epoch: 3
Accuracy: 0.5030864197530864
Epoch: 4
Accuracy: 0.4012345679012346
Epoch: 5
Accuracy: 0.5061728395061729
Epoch: 6
Accuracy: 0.5061728395061729
Epoch: 7
Accuracy: 0.5061728395061729
Epoch: 8
Accuracy: 0.5061728395061729
Epoch: 9
Accuracy: 0.5061728395061729
Finished fine tuning.
Ranking filters..
Layers that will be prunned {28: 90, 26: 64, 12: 22, 24: 87, 17: 62, 21: 52, 7: 8, 5: 8, 19: 63, 10: 24, 0: 4, 14: 26, 2: 2}
Prunning filters..
Filters prunned 63.63636363636363%
Accuracy: 0.5061728395061729
Fine tuning to recover from prunning iteration.
Epoch: 0
Accuracy: 0.5061728395061729
Epoch: 1
Accuracy: 0.49382716049382713
Epoch: 2
Accuracy: 0.49382716049382713
Epoch: 3
Accuracy: 0.6604938271604939
Epoch: 4
Accuracy: 1.0
Epoch: 5
Accuracy: 0.6790123456790124
Epoch: 6
Accuracy: 0.5061728395061729
Epoch: 7
Accuracy: 0.5061728395061729
Epoch: 8
Accuracy: 0.5679012345679012
Epoch: 9
Accuracy: 0.9845679012345679
Finished fine tuning.
Ranking filters..
Layers that will be prunned {28: 75, 14: 25, 26: 74, 21: 88, 19: 61, 7: 14, 17: 49, 24: 66, 10: 25, 0: 6, 12: 18, 5: 7, 2: 4}
Prunning filters..
Filters prunned 51.515151515151516%
Accuracy: 0.6358024691358025
Fine tuning to recover from prunning iteration.
Epoch: 0
Accuracy: 0.7623456790123457
Epoch: 1
Accuracy: 0.7623456790123457
Epoch: 2
Accuracy: 0.6851851851851852
Epoch: 3
Accuracy: 0.8950617283950617
Epoch: 4
Accuracy: 0.9660493827160493
Epoch: 5
Accuracy: 1.0
Epoch: 6
Accuracy: 0.49382716049382713
Epoch: 7
Accuracy: 0.49691358024691357
Epoch: 8
Accuracy: 0.9691358024691358
Epoch: 9
Accuracy: 0.9783950617283951
Finished fine tuning.
Ranking filters..
Layers that will be prunned {28: 73, 26: 70, 24: 82, 19: 78, 14: 35, 17: 56, 21: 49, 0: 3, 5: 11, 12: 27, 10: 19, 7: 6, 2: 3}
Prunning filters..
Filters prunned 39.39393939393939%
Accuracy: 0.9074074074074074
Fine tuning to recover from prunning iteration.
Epoch: 0
Accuracy: 0.8364197530864198
Epoch: 1
Accuracy: 0.9691358024691358
Epoch: 2
Accuracy: 0.49382716049382713
Epoch: 3
Accuracy: 0.5092592592592593
Epoch: 4
Accuracy: 0.5864197530864198
Epoch: 5
Accuracy: 0.49382716049382713
Epoch: 6
Accuracy: 0.49382716049382713
Epoch: 7
Accuracy: 1.0
Epoch: 8
Accuracy: 1.0
Epoch: 9
Accuracy: 0.8703703703703703
Finished fine tuning.
Finished. Going to fine tune the model a bit more
Epoch: 0
Accuracy: 0.49382716049382713
Epoch: 1
Accuracy: 0.8333333333333334
Epoch: 2
Accuracy: 0.7561728395061729
Epoch: 3
Accuracy: 0.49382716049382713
Epoch: 4
Accuracy: 0.5370370370370371
Epoch: 5
Accuracy: 1.0
Epoch: 6
Accuracy: 1.0
Epoch: 7
Accuracy: 0.9135802469135802
Epoch: 8
Accuracy: 0.6666666666666666
Epoch: 9
Accuracy: 1.0
Epoch: 10
Accuracy: 0.49382716049382713
Epoch: 11
Accuracy: 1.0
Epoch: 12
Accuracy: 0.49382716049382713
Epoch: 13
Accuracy: 0.49382716049382713
Epoch: 14
Accuracy: 0.49382716049382713
Finished fine tuning.
[phung@archlinux pytorch-pruning]$ ls -al
total 645652
drwxr-xr-x 6 phung phung 4096 Oct 20 23:57 .
drwxr-xr-x 6 phung phung 4096 Oct 5 08:04 ..
-rw-r--r-- 1 phung phung 1695 Oct 15 23:50 dataset.py
-rw-r--r-- 1 phung phung 9323 Oct 16 09:16 finetune.py
drwxr-xr-x 8 phung phung 4096 Oct 20 21:53 .git
-rw-r--r-- 1 phung phung 50 Oct 20 20:58 .gitignore
-rw-r--r-- 1 phung phung 537103112 Oct 20 21:49 model
-rw-r--r-- 1 phung phung 123976929 Oct 20 23:57 model_prunned
-rw-r--r-- 1 phung phung 4939 Oct 20 21:15 prune.py
drwxr-xr-x 2 phung phung 4096 Oct 20 21:18 pycache
-rw-r--r-- 1 phung phung 1488 Sep 27 22:54 README.md
drwxr-xr-x 2 phung phung 4096 Oct 20 20:55 test
drwxr-xr-x 2 phung phung 4096 Oct 20 20:54 train
[phung@archlinux pytorch-pruning]$