Conscious Artificial Intelligence - Project Update

[kapibara]

Following with interest.

/Roger

[schuler]

@Dzandaa and @kapibara, many thanks for your support!

In the case that you would like to compile/test your existing projects with the new branch (a1), this is the pull request:
https://github.com/joaopauloschuler/neural-api/pull/177

It will be nice to know if your models converge faster/slower/same with the new version. This new version works better with the AVX2 define instead of the previous default for AVX. I intend to fix bugs before merging into master.

Wish everyone happy pascal coding

[kapibara]

Here's my results. I tested master vs PR #177/a1 on a synthetic sequential geometry-detection task.

Environment:
  Linux x86_64
  FPC 3.3.1
  Laz trunk
  11th Gen Intel® Core™ i5-11600K @ 3.90GHz
  15,4 GiB of RAM

The task is binary classification over sequential numeric windows. Each sample is a 300-step window with 5 numeric features per step. The positive class represents a generated geometric shape/pattern inside the sequence; the negative class is background/no-pattern data.

Dataset:

Code: Pascal  [Select][+][-]

1. Total windows: 9,700
2. Train/Validation/Test: 5,820 / 1,940 / 1,940
3. Input shape: 300 x 5 x 1
4. Classes: 2
5. Batch size: 64
6. Epochs: 10
7.  

Training time

Code: Pascal  [Select][+][-]

1. master, no switches:             41.68 sec
2. master + -dAVX2:                 27.72 sec
3. master + -dAVX2 -O3:             27.31 sec
4.  
5. PR #177/a1, no switches:         34.03 sec
6. PR #177/a1 + -dAVX2:             19.30 sec
7. PR #177/a1 + -dAVX2 -O3:         13.09 sec
8.  

Evaluation time

Code: Pascal  [Select][+][-]

1. master, no switches:             ~7.30 sec
2. master + -O3:                     5.95 sec
3. master + -dAVX2:                  ~4.62 sec
4. master + -dAVX2 -O3:              4.25 sec
5.  
6. PR #177/a1, no switches:          5.42 sec
7. PR #177/a1 + -O3:                 3.23 sec
8. PR #177/a1 + -dAVX2:              1.74 sec
9. PR #177/a1 + -dAVX2 -O3:          0.95 sec
10.  

Quality

Code: Pascal  [Select][+][-]

1. master + -dAVX2 -O3:
2.   Accuracy:   99.02%
3.   Precision:  98.97%
4.   Recall:    100.00%
5.   F1:         99.48%
6.   FP/FN:      19 / 0
7.  
8. PR #177/a1 + -dAVX2 -O3:
9.   Accuracy:   99.28%
10.   Precision: 100.00%
11.   Recall:     99.23%
12.   F1:         99.62%
13.   FP/FN:       0 / 14
14.  

Main result:
With -dAVX2 -O3, PR #177/a1 trained about 2.1x faster and evaluated about 4.5x faster than master on this test, with similar or slightly better F1.

[schuler]

@kapibara, as a way to say “thank you”, if you have any feature that you would like coded, please feel free to ask. If not too difficult, AI will code it :-) .