neoneye/simon-arc-image-v33

Version 1

Have dataset items that are somewhat evenly of each type. The LLM learned some of the types fine. However rotated images are causing problems. The image sizes are between 1 and 10 pixels.

Version 2

Here the majority of dataset items are rotated images. Since this is what my LLM is struggling with. Smaller images. Here the image sizes are between 1 and 5 pixels. This helped a lot on the validation loss.

Version 3

Main focus is now on count_same_color_as_center_with_8neighbors_nowrap and image size 1-6. Which the LLM has struggeld with in the past, maybe due to too big image sizes. Struggles somewhat with the count_same_color_as_center_with_8neighbors_nowrap.

Version 4

I'm trying smaller images again. Here the image sizes are between 1 and 5 pixels. Added same_color_inside_3x3_area_nowrap that checks if all surrounding pixels agree on the same color, maybe that have some synergy with the count_same_color_as_center_with_8neighbors_nowrap. It helped a little, but it's still not as good at counting neighbors as I would like.

Version 5

I have added a pixels_with_k_matching_neighbors with a k parameter between 1-8. This may help improve on counting the number of neighboring pixels. The image size 1-6. This did indeed help on counting the number of surrounding pixels.

Version 6

Same weight to all the transformations. Image size 1-11.

Version 7

Focus on histogram and k-nearest neighbors. image size 1-12. It seems like the LLM has gotten the hang of it.

Version 8

Focus on histogram and k-nearest neighbors. image size 5-20.

Version 9

Focus on histogram and k-nearest neighbors. image size 10-30.

Version 10

Same weight to all the transformations. image width 10-30. image height 2-5.

Version 11

Same weight to all the transformations. image width 2-5. image height 10-30.

Version 12

Focus on k-nearest neighbors. image width 2-5. image height 10-30.

Version 13

Focus on compres_x, compres_y, compres_xy. image size is 1-10.

Version 14

Focus on histograms and k-nearest-neighbors. image size 5-20.

Version 15

Focus on histograms and k-nearest-neighbors. image size 10-30.

Version 16

Focus on k-nearest-neighbors. image size 10-25.

Version 17

Disabled k-nearest-neighbors, I suspect this is the reason why it converges so slowly. image size 15-30.

Version 18

Disabled k-nearest-neighbors, and compression. image size 15-25.

Version 19

Translate x/y by plus/minus 1. Disabled rotation and transpose. image size 22-30.

Version 20

Focus on k-nearest-neighbors. image size 5-15.

Version 21

Focus on k-nearest-neighbors. image size 8-18.

Version 22

Same weight to all the transformations. image size 8-20.

Version 23

Same weight to all the transformations. image size 5-30.

The LLM is struggling learning this. I'm going to try with small images.

Version 24

Focus on rotate cw, rotate ccw, transpose. image size 2-10.

The LLM is struggling learning this. Despite being small images. I'm going to try with even small images.

Version 25

Focus on rotate cw, rotate ccw, transpose. image size 2-5.

The LLM is struggling learning this. Despite being small images. I'm going to try with even small images.

Version 26

Focus on rotate cw, rotate ccw, transpose, k-nearest-neighbors. image size 1-3.

The LLM is struggling learning this. Despite being small images.

Version 27

Focus on rotate cw, rotate ccw, transpose. image size 1-4.

The LLM is struggling learning this. Despite being small images.

Version 28

Focus on rotate cw, rotate ccw, transpose. image size 1-5.

Version 29

Focus on rotate cw, rotate ccw, transpose. image size 1-6.

Version 30

Focus on rotate cw, rotate ccw, transpose. image size 1-8.

Version 31

Focus on rotate cw, rotate ccw, transpose. image size 1-10.

Version 32

Focus on rotate cw, rotate ccw, transpose. image size 1-12.

Version 33

Focus on rotate cw, rotate ccw, transpose. image size 1-14. The serialize items were using fewer names to identify the dataset, now uses the same names as deserialize.