cnn-vgg16.ipynb got abnormal results

[nameongithub]

Hello Sebastian,
First of all, I would like to express my gratitude for your great work and knowledge sharing!

I just ran the cnn-vgg16.ipynb on Google Colab without any modification (except the CUDA device ordinal). The result I got was totally abnormal, and was different from yours provided. The cost didn't decrease and the accuracy didn't increase at all. Could you please have a look at it?

Thank you so much, again!

Below is my train log. And the notebook on Google Colab is here.

Epoch: 001/010 | Batch 0000/0391 | Cost: 2.3682
Epoch: 001/010 | Batch 0050/0391 | Cost: 2.2857
Epoch: 001/010 | Batch 0100/0391 | Cost: 2.3016
Epoch: 001/010 | Batch 0150/0391 | Cost: 2.3024
Epoch: 001/010 | Batch 0200/0391 | Cost: 2.3069
Epoch: 001/010 | Batch 0250/0391 | Cost: 2.3022
Epoch: 001/010 | Batch 0300/0391 | Cost: 2.3035
Epoch: 001/010 | Batch 0350/0391 | Cost: 2.3035
Epoch: 001/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 0.63 min
Epoch: 002/010 | Batch 0000/0391 | Cost: 2.3032
Epoch: 002/010 | Batch 0050/0391 | Cost: 2.3020
Epoch: 002/010 | Batch 0100/0391 | Cost: 2.3012
Epoch: 002/010 | Batch 0150/0391 | Cost: 2.3041
Epoch: 002/010 | Batch 0200/0391 | Cost: 2.3035
Epoch: 002/010 | Batch 0250/0391 | Cost: 2.3009
Epoch: 002/010 | Batch 0300/0391 | Cost: 2.3026
Epoch: 002/010 | Batch 0350/0391 | Cost: 2.3005
Epoch: 002/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 1.25 min
Epoch: 003/010 | Batch 0000/0391 | Cost: 2.3008
Epoch: 003/010 | Batch 0050/0391 | Cost: 2.3013
Epoch: 003/010 | Batch 0100/0391 | Cost: 2.3013
Epoch: 003/010 | Batch 0150/0391 | Cost: 2.3018
Epoch: 003/010 | Batch 0200/0391 | Cost: 2.3027
Epoch: 003/010 | Batch 0250/0391 | Cost: 2.3029
Epoch: 003/010 | Batch 0300/0391 | Cost: 2.3028
Epoch: 003/010 | Batch 0350/0391 | Cost: 2.3036
Epoch: 003/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 1.88 min
Epoch: 004/010 | Batch 0000/0391 | Cost: 2.3025
Epoch: 004/010 | Batch 0050/0391 | Cost: 2.3021
Epoch: 004/010 | Batch 0100/0391 | Cost: 2.3015
Epoch: 004/010 | Batch 0150/0391 | Cost: 2.3024
Epoch: 004/010 | Batch 0200/0391 | Cost: 2.3027
Epoch: 004/010 | Batch 0250/0391 | Cost: 2.3014
Epoch: 004/010 | Batch 0300/0391 | Cost: 2.3030
Epoch: 004/010 | Batch 0350/0391 | Cost: 2.3026
Epoch: 004/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 2.50 min
Epoch: 005/010 | Batch 0000/0391 | Cost: 2.3014
Epoch: 005/010 | Batch 0050/0391 | Cost: 2.3027
Epoch: 005/010 | Batch 0100/0391 | Cost: 2.3023
Epoch: 005/010 | Batch 0150/0391 | Cost: 2.3017
Epoch: 005/010 | Batch 0200/0391 | Cost: 2.3007
Epoch: 005/010 | Batch 0250/0391 | Cost: 2.3018
Epoch: 005/010 | Batch 0300/0391 | Cost: 2.3029
Epoch: 005/010 | Batch 0350/0391 | Cost: 2.3028
Epoch: 005/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 3.13 min
Epoch: 006/010 | Batch 0000/0391 | Cost: 2.3018
Epoch: 006/010 | Batch 0050/0391 | Cost: 2.3009
Epoch: 006/010 | Batch 0100/0391 | Cost: 2.3020
Epoch: 006/010 | Batch 0150/0391 | Cost: 2.3030
Epoch: 006/010 | Batch 0200/0391 | Cost: 2.3025
Epoch: 006/010 | Batch 0250/0391 | Cost: 2.3005
Epoch: 006/010 | Batch 0300/0391 | Cost: 2.3033
Epoch: 006/010 | Batch 0350/0391 | Cost: 2.3028
Epoch: 006/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 3.75 min
Epoch: 007/010 | Batch 0000/0391 | Cost: 2.3024
Epoch: 007/010 | Batch 0050/0391 | Cost: 2.3027
Epoch: 007/010 | Batch 0100/0391 | Cost: 2.3032
Epoch: 007/010 | Batch 0150/0391 | Cost: 2.3044
Epoch: 007/010 | Batch 0200/0391 | Cost: 2.3026
Epoch: 007/010 | Batch 0250/0391 | Cost: 2.3030
Epoch: 007/010 | Batch 0300/0391 | Cost: 2.3026
Epoch: 007/010 | Batch 0350/0391 | Cost: 2.3024
Epoch: 007/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 4.37 min
Epoch: 008/010 | Batch 0000/0391 | Cost: 2.3025
Epoch: 008/010 | Batch 0050/0391 | Cost: 2.3033
Epoch: 008/010 | Batch 0100/0391 | Cost: 2.3034
Epoch: 008/010 | Batch 0150/0391 | Cost: 2.3021
Epoch: 008/010 | Batch 0200/0391 | Cost: 2.3034
Epoch: 008/010 | Batch 0250/0391 | Cost: 2.3034
Epoch: 008/010 | Batch 0300/0391 | Cost: 2.3027
Epoch: 008/010 | Batch 0350/0391 | Cost: 2.3030
Epoch: 008/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 5.00 min
Epoch: 009/010 | Batch 0000/0391 | Cost: 2.3031
Epoch: 009/010 | Batch 0050/0391 | Cost: 2.3029
Epoch: 009/010 | Batch 0100/0391 | Cost: 2.3033
Epoch: 009/010 | Batch 0150/0391 | Cost: 2.3035
Epoch: 009/010 | Batch 0200/0391 | Cost: 2.3019
Epoch: 009/010 | Batch 0250/0391 | Cost: 2.3027
Epoch: 009/010 | Batch 0300/0391 | Cost: 2.3037
Epoch: 009/010 | Batch 0350/0391 | Cost: 2.3027
Epoch: 009/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 5.62 min
Epoch: 010/010 | Batch 0000/0391 | Cost: 2.3030
Epoch: 010/010 | Batch 0050/0391 | Cost: 2.3023
Epoch: 010/010 | Batch 0100/0391 | Cost: 2.3031
Epoch: 010/010 | Batch 0150/0391 | Cost: 2.3023
Epoch: 010/010 | Batch 0200/0391 | Cost: 2.3029
Epoch: 010/010 | Batch 0250/0391 | Cost: 2.3022
Epoch: 010/010 | Batch 0300/0391 | Cost: 2.3023
Epoch: 010/010 | Batch 0350/0391 | Cost: 2.3029
Epoch: 010/010 | Train: 10.000% |  Loss: 2.303
Time elapsed: 6.25 min
Total Training Time: 6.25 min