man python stinks...

updated comment for PairwiseComparator
renamed ADAMW_WiDECAY
2025-12-25 14:54:08 +00:00 · 2025-12-25 14:53:35 +00:00 · 2025-12-25 14:48:07 +00:00 · 2025-12-25 02:13:23 +00:00 · 2025-12-25 02:07:49 +00:00 · 2025-12-25 01:52:19 +00:00
7 changed files with 284 additions and 677 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
 __pycache__/
 files/
 .venv/
-*.pth
+*.png
 *.model
 *.log
--- a/README.md
+++ b/README.md
@@ -25,11 +25,11 @@ source .venv/bin/activate
 For instructions installing pytorch refer to the [PyTorch Home page]
 ```bash
-pip3 install numpy
+pip3 install numpy pandas matplotlib
-# use the nvidia CUDA or CPU only packages if required.
+# I use the ROCm packages
 # I use the ROCm packages, so the repo uses the ROCm packages.
 pip3 install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.4
-pip3 install pandas matplotlib
+# if you need the CPU only package
 # pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
 ```
 ## Running the code
--- a/infer.py
+++ b/infer.py
@@ -1 +0,0 @@
 pairwise_compare.py
--- a/output_graphs.py
+++ b/output_graphs.py
@@ -1,573 +0,0 @@
 import re
 import pandas as pd
 import matplotlib.pyplot as plt
 text = r"""INFO:__main__:step=    0 loss=1.3149878 acc=0.9093018
 INFO:__main__:step=    1 loss=1.0067503 acc=0.9183350
 INFO:__main__:step=    2 loss=0.7588759 acc=0.9278564
 INFO:__main__:step=    3 loss=0.5432727 acc=0.9384766
 INFO:__main__:step=    4 loss=0.3854972 acc=0.9427490
 INFO:__main__:step=    5 loss=0.2379047 acc=0.9512939
 INFO:__main__:step=    6 loss=0.1357967 acc=0.9575195
 INFO:__main__:step=    7 loss=0.0659471 acc=0.9808960
 INFO:__main__:step=    8 loss=0.0325629 acc=0.9878540
 INFO:__main__:step=    9 loss=0.0159342 acc=0.9951172
 INFO:__main__:step=   10 loss=0.0123130 acc=0.9995117
 INFO:__main__:step=   11 loss=0.0129503 acc=0.9963989
 INFO:__main__:step=   12 loss=0.0198095 acc=0.9927979
 INFO:__main__:step=   13 loss=0.0261302 acc=0.9901733
 INFO:__main__:step=   14 loss=0.0338637 acc=0.9891968
 INFO:__main__:step=   15 loss=0.0425015 acc=0.9861450
 INFO:__main__:step=   16 loss=0.0458022 acc=0.9865112
 INFO:__main__:step=   17 loss=0.0492692 acc=0.9859619
 INFO:__main__:step=   18 loss=0.0543331 acc=0.9850464
 INFO:__main__:step=   19 loss=0.0575214 acc=0.9846802
 INFO:__main__:step=   20 loss=0.0635471 acc=0.9841919
 INFO:__main__:step=   21 loss=0.0503580 acc=0.9871216
 INFO:__main__:step=   22 loss=0.0503206 acc=0.9863892
 INFO:__main__:step=   23 loss=0.0549617 acc=0.9852905
 INFO:__main__:step=   24 loss=0.0438731 acc=0.9872437
 INFO:__main__:step=   25 loss=0.0471392 acc=0.9867554
 INFO:__main__:step=   26 loss=0.0431895 acc=0.9868774
 INFO:__main__:step=   27 loss=0.0354491 acc=0.9892578
 INFO:__main__:step=   28 loss=0.0311145 acc=0.9905396
 INFO:__main__:step=   29 loss=0.0305991 acc=0.9897461
 INFO:__main__:step=   30 loss=0.0229458 acc=0.9920044
 INFO:__main__:step=   31 loss=0.0203844 acc=0.9925537
 INFO:__main__:step=   32 loss=0.0190371 acc=0.9929810
 INFO:__main__:step=   33 loss=0.0167215 acc=0.9935303
 INFO:__main__:step=   34 loss=0.0153083 acc=0.9937744
 INFO:__main__:step=   35 loss=0.0120912 acc=0.9947510
 INFO:__main__:step=   36 loss=0.0101961 acc=0.9961548
 INFO:__main__:step=   37 loss=0.0100932 acc=0.9968262
 INFO:__main__:step=   38 loss=0.0101871 acc=0.9968262
 INFO:__main__:step=   39 loss=0.0088590 acc=0.9978027
 INFO:__main__:step=   40 loss=0.0090834 acc=0.9982300
 INFO:__main__:step=   41 loss=0.0084685 acc=0.9993896
 INFO:__main__:step=   42 loss=0.0075352 acc=0.9989014
 INFO:__main__:step=   43 loss=0.0080332 acc=0.9987793
 INFO:__main__:step=   44 loss=0.0087203 acc=0.9979858
 INFO:__main__:step=   45 loss=0.0086979 acc=0.9977417
 INFO:__main__:step=   46 loss=0.0086313 acc=0.9972534
 INFO:__main__:step=   47 loss=0.0092596 acc=0.9971924
 INFO:__main__:step=   48 loss=0.0086056 acc=0.9978027
 INFO:__main__:step=   49 loss=0.0101687 acc=0.9960327
 INFO:__main__:step=   50 loss=0.0083168 acc=0.9978638
 INFO:__main__:step=   51 loss=0.0095370 acc=0.9965820
 INFO:__main__:step=   52 loss=0.0093318 acc=0.9968872
 INFO:__main__:step=   53 loss=0.0093573 acc=0.9966431
 INFO:__main__:step=   54 loss=0.0091653 acc=0.9971313
 INFO:__main__:step=   55 loss=0.0097087 acc=0.9962769
 INFO:__main__:step=   56 loss=0.0094240 acc=0.9972534
 INFO:__main__:step=   57 loss=0.0088949 acc=0.9970093
 INFO:__main__:step=   58 loss=0.0085519 acc=0.9975586
 INFO:__main__:step=   59 loss=0.0098900 acc=0.9967651
 INFO:__main__:step=   60 loss=0.0085984 acc=0.9977417
 INFO:__main__:step=   61 loss=0.0082639 acc=0.9984131
 INFO:__main__:step=   62 loss=0.0083590 acc=0.9979858
 INFO:__main__:step=   63 loss=0.0072852 acc=0.9990234
 INFO:__main__:step=   64 loss=0.0080845 acc=0.9983521
 INFO:__main__:step=   65 loss=0.0074006 acc=0.9988403
 INFO:__main__:step=   66 loss=0.0067656 acc=0.9990845
 INFO:__main__:step=   67 loss=0.0072893 acc=0.9992065
 INFO:__main__:step=   68 loss=0.0077519 acc=0.9991455
 INFO:__main__:step=   69 loss=0.0072619 acc=0.9992065
 INFO:__main__:step=   70 loss=0.0069199 acc=0.9993896
 INFO:__main__:step=   71 loss=0.0070979 acc=0.9992676
 INFO:__main__:step=   72 loss=0.0073295 acc=0.9992676
 INFO:__main__:step=   73 loss=0.0074595 acc=0.9994507
 INFO:__main__:step=   74 loss=0.0069510 acc=0.9993896
 INFO:__main__:step=   75 loss=0.0072545 acc=0.9993896
 INFO:__main__:step=   76 loss=0.0070704 acc=0.9994507
 INFO:__main__:step=   77 loss=0.0080527 acc=0.9992065
 INFO:__main__:step=   78 loss=0.0078222 acc=0.9990234
 INFO:__main__:step=   79 loss=0.0074403 acc=0.9993896
 INFO:__main__:step=   80 loss=0.0070136 acc=0.9992065
 INFO:__main__:step=   81 loss=0.0068662 acc=0.9992676
 INFO:__main__:step=   82 loss=0.0071025 acc=0.9992676
 INFO:__main__:step=   83 loss=0.0068718 acc=0.9993896
 INFO:__main__:step=   84 loss=0.0072839 acc=0.9991455
 INFO:__main__:step=   85 loss=0.0071138 acc=0.9992676
 INFO:__main__:step=   86 loss=0.0070522 acc=0.9992065
 INFO:__main__:step=   87 loss=0.0073670 acc=0.9992065
 INFO:__main__:step=   88 loss=0.0069347 acc=0.9995117
 INFO:__main__:step=   89 loss=0.0066190 acc=0.9993286
 INFO:__main__:step=   90 loss=0.0069868 acc=0.9997559
 INFO:__main__:step=   91 loss=0.0071491 acc=0.9995728
 INFO:__main__:step=   92 loss=0.0071107 acc=0.9991455
 INFO:__main__:step=   93 loss=0.0065876 acc=0.9996338
 INFO:__main__:step=   94 loss=0.0067994 acc=0.9995117
 INFO:__main__:step=   95 loss=0.0072922 acc=0.9995728
 INFO:__main__:step=   96 loss=0.0070875 acc=0.9995117
 INFO:__main__:step=   97 loss=0.0067269 acc=0.9996338
 INFO:__main__:step=   98 loss=0.0069193 acc=0.9995728
 INFO:__main__:step=   99 loss=0.0068322 acc=0.9995728
 INFO:__main__:step=  100 loss=0.0065636 acc=0.9995117
 INFO:__main__:step=  101 loss=0.0072776 acc=0.9995117
 INFO:__main__:step=  102 loss=0.0074568 acc=0.9996338
 INFO:__main__:step=  103 loss=0.0067574 acc=0.9996338
 INFO:__main__:step=  104 loss=0.0067869 acc=0.9995728
 INFO:__main__:step=  105 loss=0.0064811 acc=0.9996948
 INFO:__main__:step=  106 loss=0.0077020 acc=0.9990234
 INFO:__main__:step=  107 loss=0.0069068 acc=0.9996338
 INFO:__main__:step=  108 loss=0.0068666 acc=0.9994507
 INFO:__main__:step=  109 loss=0.0066869 acc=0.9992065
 INFO:__main__:step=  110 loss=0.0070414 acc=0.9993286
 INFO:__main__:step=  111 loss=0.0072129 acc=0.9994507
 INFO:__main__:step=  112 loss=0.0067772 acc=0.9993896
 INFO:__main__:step=  113 loss=0.0069291 acc=0.9995728
 INFO:__main__:step=  114 loss=0.0065003 acc=0.9996338
 INFO:__main__:step=  115 loss=0.0071769 acc=0.9993286
 INFO:__main__:step=  116 loss=0.0068795 acc=0.9993896
 INFO:__main__:step=  117 loss=0.0068955 acc=0.9995117
 INFO:__main__:step=  118 loss=0.0071484 acc=0.9993896
 INFO:__main__:step=  119 loss=0.0072527 acc=0.9992676
 INFO:__main__:step=  120 loss=0.0070072 acc=0.9996948
 INFO:__main__:step=  121 loss=0.0069677 acc=0.9995117
 INFO:__main__:step=  122 loss=0.0070129 acc=0.9996338
 INFO:__main__:step=  123 loss=0.0068947 acc=0.9990845
 INFO:__main__:step=  124 loss=0.0066504 acc=0.9996338
 INFO:__main__:step=  125 loss=0.0060845 acc=0.9994507
 INFO:__main__:step=  126 loss=0.0063553 acc=0.9995117
 INFO:__main__:step=  127 loss=0.0063697 acc=0.9996948
 INFO:__main__:step=  128 loss=0.0064274 acc=0.9995728
 INFO:__main__:step=  129 loss=0.0057200 acc=0.9998169
 INFO:__main__:step=  130 loss=0.0064411 acc=0.9996948
 INFO:__main__:step=  131 loss=0.0060729 acc=0.9998779
 INFO:__main__:step=  132 loss=0.0062524 acc=0.9996948
 INFO:__main__:step=  133 loss=0.0060491 acc=0.9991455
 INFO:__main__:step=  134 loss=0.0065517 acc=0.9996338
 INFO:__main__:step=  135 loss=0.0058316 acc=0.9998169
 INFO:__main__:step=  136 loss=0.0059018 acc=0.9997559
 INFO:__main__:step=  137 loss=0.0065307 acc=0.9996948
 INFO:__main__:step=  138 loss=0.0064329 acc=0.9995117
 INFO:__main__:step=  139 loss=0.0066867 acc=0.9995728
 INFO:__main__:step=  140 loss=0.0070452 acc=0.9992676
 INFO:__main__:step=  141 loss=0.0064459 acc=0.9993896
 INFO:__main__:step=  142 loss=0.0061031 acc=0.9997559
 INFO:__main__:step=  143 loss=0.0067490 acc=0.9992676
 INFO:__main__:step=  144 loss=0.0065364 acc=0.9998779
 INFO:__main__:step=  145 loss=0.0058783 acc=0.9993286
 INFO:__main__:step=  146 loss=0.0060647 acc=0.9998169
 INFO:__main__:step=  147 loss=0.0064641 acc=0.9996338
 INFO:__main__:step=  148 loss=0.0065979 acc=0.9996338
 INFO:__main__:step=  149 loss=0.0058760 acc=0.9997559
 INFO:__main__:step=  150 loss=0.0058440 acc=0.9998779
 INFO:__main__:step=  151 loss=0.0060483 acc=0.9998169
 INFO:__main__:step=  152 loss=0.0062729 acc=0.9998169
 INFO:__main__:step=  153 loss=0.0061169 acc=0.9996338
 INFO:__main__:step=  154 loss=0.0064641 acc=0.9995728
 INFO:__main__:step=  155 loss=0.0062200 acc=0.9995117
 INFO:__main__:step=  156 loss=0.0062547 acc=0.9997559
 INFO:__main__:step=  157 loss=0.0064813 acc=0.9995728
 INFO:__main__:step=  158 loss=0.0061465 acc=0.9996338
 INFO:__main__:step=  159 loss=0.0056798 acc=0.9995728
 INFO:__main__:step=  160 loss=0.0064472 acc=0.9996338
 INFO:__main__:step=  161 loss=0.0055585 acc=0.9997559
 INFO:__main__:step=  162 loss=0.0057363 acc=0.9995117
 INFO:__main__:step=  163 loss=0.0061281 acc=0.9995728
 INFO:__main__:step=  164 loss=0.0062811 acc=0.9995117
 INFO:__main__:step=  165 loss=0.0058447 acc=0.9997559
 INFO:__main__:step=  166 loss=0.0054476 acc=0.9998169
 INFO:__main__:step=  167 loss=0.0067439 acc=0.9998779
 INFO:__main__:step=  168 loss=0.0063363 acc=0.9992676
 INFO:__main__:step=  169 loss=0.0060227 acc=0.9995117
 INFO:__main__:step=  170 loss=0.0060772 acc=0.9998169
 INFO:__main__:step=  171 loss=0.0060449 acc=0.9994507
 INFO:__main__:step=  172 loss=0.0061583 acc=0.9997559
 INFO:__main__:step=  173 loss=0.0061034 acc=0.9995728
 INFO:__main__:step=  174 loss=0.0060934 acc=0.9996948
 INFO:__main__:step=  175 loss=0.0062173 acc=0.9995117
 INFO:__main__:step=  176 loss=0.0065296 acc=0.9995728
 INFO:__main__:step=  177 loss=0.0067725 acc=0.9994507
 INFO:__main__:step=  178 loss=0.0064399 acc=0.9997559
 INFO:__main__:step=  179 loss=0.0060964 acc=0.9997559
 INFO:__main__:step=  180 loss=0.0053458 acc=0.9995728
 INFO:__main__:step=  181 loss=0.0056739 acc=0.9996338
 INFO:__main__:step=  182 loss=0.0054851 acc=0.9996338
 INFO:__main__:step=  183 loss=0.0064169 acc=0.9997559
 INFO:__main__:step=  184 loss=0.0058324 acc=0.9996948
 INFO:__main__:step=  185 loss=0.0058293 acc=0.9994507
 INFO:__main__:step=  186 loss=0.0067926 acc=0.9996338
 INFO:__main__:step=  187 loss=0.0053189 acc=0.9994507
 INFO:__main__:step=  188 loss=0.0059640 acc=0.9998169
 INFO:__main__:step=  189 loss=0.0055875 acc=0.9996338
 INFO:__main__:step=  190 loss=0.0057980 acc=0.9995117
 INFO:__main__:step=  191 loss=0.0057864 acc=0.9996338
 INFO:__main__:step=  192 loss=0.0052436 acc=0.9999390
 INFO:__main__:step=  193 loss=0.0063448 acc=0.9994507
 INFO:__main__:step=  194 loss=0.0056429 acc=0.9996948
 INFO:__main__:step=  195 loss=0.0057287 acc=0.9993286
 INFO:__main__:step=  196 loss=0.0058553 acc=0.9998169
 INFO:__main__:step=  197 loss=0.0058331 acc=0.9992676
 INFO:__main__:step=  198 loss=0.0052676 acc=0.9995117
 INFO:__main__:step=  199 loss=0.0058648 acc=0.9996948
 INFO:__main__:step=  200 loss=0.0061336 acc=0.9998169
 INFO:__main__:step=  201 loss=0.0053004 acc=0.9998169
 INFO:__main__:step=  202 loss=0.0061638 acc=0.9997559
 INFO:__main__:step=  203 loss=0.0065025 acc=0.9995117
 INFO:__main__:step=  204 loss=0.0059157 acc=0.9998169
 INFO:__main__:step=  205 loss=0.0054036 acc=0.9998779
 INFO:__main__:step=  206 loss=0.0056138 acc=0.9996338
 INFO:__main__:step=  207 loss=0.0054218 acc=0.9996338
 INFO:__main__:step=  208 loss=0.0066803 acc=0.9992676
 INFO:__main__:step=  209 loss=0.0058715 acc=0.9996948
 INFO:__main__:step=  210 loss=0.0055717 acc=0.9998169
 INFO:__main__:step=  211 loss=0.0060626 acc=0.9994507
 INFO:__main__:step=  212 loss=0.0051552 acc=0.9998779
 INFO:__main__:step=  213 loss=0.0059606 acc=0.9995117
 INFO:__main__:step=  214 loss=0.0049876 acc=0.9998779
 INFO:__main__:step=  215 loss=0.0057766 acc=0.9996338
 INFO:__main__:step=  216 loss=0.0060298 acc=0.9996338
 INFO:__main__:step=  217 loss=0.0055154 acc=0.9996338
 INFO:__main__:step=  218 loss=0.0055578 acc=0.9998169
 INFO:__main__:step=  219 loss=0.0052298 acc=0.9995728
 INFO:__main__:step=  220 loss=0.0064167 acc=0.9995117
 INFO:__main__:step=  221 loss=0.0056415 acc=0.9996338
 INFO:__main__:step=  222 loss=0.0052709 acc=0.9995728
 INFO:__main__:step=  223 loss=0.0060531 acc=0.9995728
 INFO:__main__:step=  224 loss=0.0049705 acc=0.9995117
 INFO:__main__:step=  225 loss=0.0053588 acc=0.9997559
 INFO:__main__:step=  226 loss=0.0052681 acc=0.9998779
 INFO:__main__:step=  227 loss=0.0053794 acc=0.9996948
 INFO:__main__:step=  228 loss=0.0056610 acc=0.9997559
 INFO:__main__:step=  229 loss=0.0057926 acc=0.9995728
 INFO:__main__:step=  230 loss=0.0053835 acc=0.9998169
 INFO:__main__:step=  231 loss=0.0045384 acc=0.9996948
 INFO:__main__:step=  232 loss=0.0048465 acc=0.9997559
 INFO:__main__:step=  233 loss=0.0057317 acc=0.9995117
 INFO:__main__:step=  234 loss=0.0058601 acc=0.9995117
 INFO:__main__:step=  235 loss=0.0050546 acc=0.9997559
 INFO:__main__:step=  236 loss=0.0049725 acc=1.0000000
 INFO:__main__:step=  237 loss=0.0048411 acc=0.9995728
 INFO:__main__:step=  238 loss=0.0054285 acc=0.9998169
 INFO:__main__:step=  239 loss=0.0051857 acc=0.9997559
 INFO:__main__:step=  240 loss=0.0054422 acc=0.9996948
 INFO:__main__:step=  241 loss=0.0054665 acc=0.9999390
 INFO:__main__:step=  242 loss=0.0049092 acc=0.9996948
 INFO:__main__:step=  243 loss=0.0050945 acc=0.9995728
 INFO:__main__:step=  244 loss=0.0057586 acc=0.9992676
 INFO:__main__:step=  245 loss=0.0055652 acc=0.9997559
 INFO:__main__:step=  246 loss=0.0052052 acc=0.9996948
 INFO:__main__:step=  247 loss=0.0050273 acc=0.9996948
 INFO:__main__:step=  248 loss=0.0055413 acc=0.9994507
 INFO:__main__:step=  249 loss=0.0050175 acc=0.9998169
 INFO:__main__:step=  250 loss=0.0049932 acc=0.9998169
 INFO:__main__:step=  251 loss=0.0050813 acc=0.9997559
 INFO:__main__:step=  252 loss=0.0051735 acc=0.9998169
 INFO:__main__:step=  253 loss=0.0049972 acc=0.9998169
 INFO:__main__:step=  254 loss=0.0051353 acc=0.9996338
 INFO:__main__:step=  255 loss=0.0052580 acc=0.9996338
 INFO:__main__:step=  256 loss=0.0047390 acc=0.9996948
 INFO:__main__:step=  257 loss=0.0051188 acc=0.9997559
 INFO:__main__:step=  258 loss=0.0046280 acc=0.9997559
 INFO:__main__:step=  259 loss=0.0047725 acc=0.9997559
 INFO:__main__:step=  260 loss=0.0045635 acc=0.9996338
 INFO:__main__:step=  261 loss=0.0053751 acc=0.9995728
 INFO:__main__:step=  262 loss=0.0046179 acc=0.9998779
 INFO:__main__:step=  263 loss=0.0052214 acc=0.9994507
 INFO:__main__:step=  264 loss=0.0044471 acc=0.9996948
 INFO:__main__:step=  265 loss=0.0053352 acc=0.9997559
 INFO:__main__:step=  266 loss=0.0053903 acc=0.9998779
 INFO:__main__:step=  267 loss=0.0050240 acc=0.9996948
 INFO:__main__:step=  268 loss=0.0048375 acc=0.9996948
 INFO:__main__:step=  269 loss=0.0055583 acc=0.9995117
 INFO:__main__:step=  270 loss=0.0047512 acc=1.0000000
 INFO:__main__:step=  271 loss=0.0045881 acc=0.9998779
 INFO:__main__:step=  272 loss=0.0045754 acc=0.9996338
 INFO:__main__:step=  273 loss=0.0051980 acc=0.9996338
 INFO:__main__:step=  274 loss=0.0048658 acc=0.9997559
 INFO:__main__:step=  275 loss=0.0049445 acc=0.9996338
 INFO:__main__:step=  276 loss=0.0051786 acc=0.9998779
 INFO:__main__:step=  277 loss=0.0045741 acc=0.9997559
 INFO:__main__:step=  278 loss=0.0054667 acc=0.9995728
 INFO:__main__:step=  279 loss=0.0049829 acc=0.9998169
 INFO:__main__:step=  280 loss=0.0049411 acc=0.9995117
 INFO:__main__:step=  281 loss=0.0051374 acc=0.9998779
 INFO:__main__:step=  282 loss=0.0046585 acc=0.9996338
 INFO:__main__:step=  283 loss=0.0049610 acc=0.9997559
 INFO:__main__:step=  284 loss=0.0044862 acc=0.9998779
 INFO:__main__:step=  285 loss=0.0050020 acc=0.9996948
 INFO:__main__:step=  286 loss=0.0043370 acc=0.9999390
 INFO:__main__:step=  287 loss=0.0051258 acc=0.9996338
 INFO:__main__:step=  288 loss=0.0043914 acc=0.9998779
 INFO:__main__:step=  289 loss=0.0043173 acc=0.9996948
 INFO:__main__:step=  290 loss=0.0047723 acc=0.9998779
 INFO:__main__:step=  291 loss=0.0049867 acc=0.9996948
 INFO:__main__:step=  292 loss=0.0046409 acc=0.9998169
 INFO:__main__:step=  293 loss=0.0039387 acc=1.0000000
 INFO:__main__:step=  294 loss=0.0051583 acc=0.9994507
 INFO:__main__:step=  295 loss=0.0043871 acc=0.9998169
 INFO:__main__:step=  296 loss=0.0046067 acc=0.9997559
 INFO:__main__:step=  297 loss=0.0043712 acc=0.9996948
 INFO:__main__:step=  298 loss=0.0047873 acc=0.9995117
 INFO:__main__:step=  299 loss=0.0047681 acc=0.9998779
 INFO:__main__:step=  300 loss=0.0050251 acc=0.9997559
 INFO:__main__:step=  301 loss=0.0045633 acc=0.9998169
 INFO:__main__:step=  302 loss=0.0043650 acc=0.9999390
 INFO:__main__:step=  303 loss=0.0049224 acc=0.9997559
 INFO:__main__:step=  304 loss=0.0049409 acc=0.9998169
 INFO:__main__:step=  305 loss=0.0052364 acc=0.9996948
 INFO:__main__:step=  306 loss=0.0051323 acc=0.9995728
 INFO:__main__:step=  307 loss=0.0047827 acc=0.9997559
 INFO:__main__:step=  308 loss=0.0044320 acc=0.9997559
 INFO:__main__:step=  309 loss=0.0050638 acc=0.9997559
 INFO:__main__:step=  310 loss=0.0052357 acc=0.9997559
 INFO:__main__:step=  311 loss=0.0044889 acc=0.9995728
 INFO:__main__:step=  312 loss=0.0043897 acc=0.9997559
 INFO:__main__:step=  313 loss=0.0050018 acc=0.9998779
 INFO:__main__:step=  314 loss=0.0043691 acc=0.9998169
 INFO:__main__:step=  315 loss=0.0042810 acc=0.9999390
 INFO:__main__:step=  316 loss=0.0045961 acc=0.9996338
 INFO:__main__:step=  317 loss=0.0047868 acc=0.9998169
 INFO:__main__:step=  318 loss=0.0047044 acc=0.9998169
 INFO:__main__:step=  319 loss=0.0046348 acc=0.9998169
 INFO:__main__:step=  320 loss=0.0043805 acc=0.9998779
 INFO:__main__:step=  321 loss=0.0041868 acc=0.9997559
 INFO:__main__:step=  322 loss=0.0045165 acc=0.9997559
 INFO:__main__:step=  323 loss=0.0050955 acc=0.9997559
 INFO:__main__:step=  324 loss=0.0046222 acc=0.9996948
 INFO:__main__:step=  325 loss=0.0045308 acc=0.9996338
 INFO:__main__:step=  326 loss=0.0052673 acc=0.9993896
 INFO:__main__:step=  327 loss=0.0049429 acc=0.9995728
 INFO:__main__:step=  328 loss=0.0042904 acc=0.9997559
 INFO:__main__:step=  329 loss=0.0047324 acc=0.9996948
 INFO:__main__:step=  330 loss=0.0049455 acc=0.9996338
 INFO:__main__:step=  331 loss=0.0042475 acc=0.9998779
 INFO:__main__:step=  332 loss=0.0045173 acc=0.9996948
 INFO:__main__:step=  333 loss=0.0047958 acc=0.9996338
 INFO:__main__:step=  334 loss=0.0045062 acc=0.9996948
 INFO:__main__:step=  335 loss=0.0043826 acc=0.9996338
 INFO:__main__:step=  336 loss=0.0047664 acc=0.9998169
 INFO:__main__:step=  337 loss=0.0048210 acc=0.9997559
 INFO:__main__:step=  338 loss=0.0048734 acc=0.9996338
 INFO:__main__:step=  339 loss=0.0038904 acc=0.9998169
 INFO:__main__:step=  340 loss=0.0046818 acc=0.9993896
 INFO:__main__:step=  341 loss=0.0050763 acc=0.9995728
 INFO:__main__:step=  342 loss=0.0042104 acc=0.9998779
 INFO:__main__:step=  343 loss=0.0045529 acc=0.9996338
 INFO:__main__:step=  344 loss=0.0044936 acc=0.9997559
 INFO:__main__:step=  345 loss=0.0045376 acc=0.9998779
 INFO:__main__:step=  346 loss=0.0046967 acc=0.9998169
 INFO:__main__:step=  347 loss=0.0042559 acc=0.9995728
 INFO:__main__:step=  348 loss=0.0049302 acc=0.9993286
 INFO:__main__:step=  349 loss=0.0045091 acc=0.9996948
 INFO:__main__:step=  350 loss=0.0043695 acc=0.9996948
 INFO:__main__:step=  351 loss=0.0048456 acc=0.9995117
 INFO:__main__:step=  352 loss=0.0043468 acc=0.9996948
 INFO:__main__:step=  353 loss=0.0044966 acc=0.9998779
 INFO:__main__:step=  354 loss=0.0042396 acc=0.9998169
 INFO:__main__:step=  355 loss=0.0048033 acc=0.9994507
 INFO:__main__:step=  356 loss=0.0043947 acc=0.9997559
 INFO:__main__:step=  357 loss=0.0042306 acc=0.9996338
 INFO:__main__:step=  358 loss=0.0040438 acc=0.9998169
 INFO:__main__:step=  359 loss=0.0046469 acc=0.9997559
 INFO:__main__:step=  360 loss=0.0044782 acc=0.9998169
 INFO:__main__:step=  361 loss=0.0045767 acc=0.9995117
 INFO:__main__:step=  362 loss=0.0044509 acc=0.9998169
 INFO:__main__:step=  363 loss=0.0049030 acc=0.9996948
 INFO:__main__:step=  364 loss=0.0042743 acc=0.9998779
 INFO:__main__:step=  365 loss=0.0043227 acc=0.9998779
 INFO:__main__:step=  366 loss=0.0045902 acc=0.9996338
 INFO:__main__:step=  367 loss=0.0040066 acc=1.0000000
 INFO:__main__:step=  368 loss=0.0041235 acc=0.9998779
 INFO:__main__:step=  369 loss=0.0042298 acc=0.9998779
 INFO:__main__:step=  370 loss=0.0041855 acc=0.9996948
 INFO:__main__:step=  371 loss=0.0042327 acc=0.9999390
 INFO:__main__:step=  372 loss=0.0045756 acc=0.9998169
 INFO:__main__:step=  373 loss=0.0041091 acc=0.9999390
 INFO:__main__:step=  374 loss=0.0046337 acc=0.9995728
 INFO:__main__:step=  375 loss=0.0048172 acc=0.9996338
 INFO:__main__:step=  376 loss=0.0049001 acc=0.9997559
 INFO:__main__:step=  377 loss=0.0047032 acc=0.9998779
 INFO:__main__:step=  378 loss=0.0039463 acc=0.9997559
 INFO:__main__:step=  379 loss=0.0043672 acc=0.9997559
 INFO:__main__:step=  380 loss=0.0043338 acc=0.9995728
 INFO:__main__:step=  381 loss=0.0044772 acc=0.9996948
 INFO:__main__:step=  382 loss=0.0040340 acc=0.9997559
 INFO:__main__:step=  383 loss=0.0042726 acc=0.9997559
 INFO:__main__:step=  384 loss=0.0042994 acc=0.9998779
 INFO:__main__:step=  385 loss=0.0040821 acc=0.9996948
 INFO:__main__:step=  386 loss=0.0036824 acc=0.9998779
 INFO:__main__:step=  387 loss=0.0049368 acc=0.9998169
 INFO:__main__:step=  388 loss=0.0040065 acc=0.9997559
 INFO:__main__:step=  389 loss=0.0043364 acc=0.9998779
 INFO:__main__:step=  390 loss=0.0042396 acc=0.9997559
 INFO:__main__:step=  391 loss=0.0041326 acc=0.9999390
 INFO:__main__:step=  392 loss=0.0045820 acc=0.9998169
 INFO:__main__:step=  393 loss=0.0045091 acc=0.9998779
 INFO:__main__:step=  394 loss=0.0041117 acc=0.9998169
 INFO:__main__:step=  395 loss=0.0037867 acc=0.9996948
 INFO:__main__:step=  396 loss=0.0041869 acc=0.9998169
 INFO:__main__:step=  397 loss=0.0041473 acc=0.9996948
 INFO:__main__:step=  398 loss=0.0045813 acc=0.9995728
 INFO:__main__:step=  399 loss=0.0037894 acc=0.9998779
 INFO:__main__:step=  400 loss=0.0042846 acc=0.9998169
 INFO:__main__:step=  401 loss=0.0039527 acc=0.9996338
 INFO:__main__:step=  402 loss=0.0042966 acc=0.9998169
 INFO:__main__:step=  403 loss=0.0034902 acc=0.9998169
 INFO:__main__:step=  404 loss=0.0039595 acc=0.9998169
 INFO:__main__:step=  405 loss=0.0036952 acc=0.9998779
 INFO:__main__:step=  406 loss=0.0039154 acc=0.9998779
 INFO:__main__:step=  407 loss=0.0041118 acc=0.9998169
 INFO:__main__:step=  408 loss=0.0037084 acc=0.9997559
 INFO:__main__:step=  409 loss=0.0038251 acc=0.9998779
 INFO:__main__:step=  410 loss=0.0042379 acc=0.9996948
 INFO:__main__:step=  411 loss=0.0044292 acc=0.9996338
 INFO:__main__:step=  412 loss=0.0048427 acc=1.0000000
 INFO:__main__:step=  413 loss=0.0044034 acc=0.9996948
 INFO:__main__:step=  414 loss=0.0039434 acc=0.9998779
 INFO:__main__:step=  415 loss=0.0036064 acc=0.9999390
 INFO:__main__:step=  416 loss=0.0043831 acc=0.9998169
 INFO:__main__:step=  417 loss=0.0036476 acc=0.9999390
 INFO:__main__:step=  418 loss=0.0039738 acc=0.9998779
 INFO:__main__:step=  419 loss=0.0043787 acc=0.9996948
 INFO:__main__:step=  420 loss=0.0042686 acc=0.9996948
 INFO:__main__:step=  421 loss=0.0045272 acc=0.9998169
 INFO:__main__:step=  422 loss=0.0043030 acc=0.9998779
 INFO:__main__:step=  423 loss=0.0043391 acc=0.9996338
 INFO:__main__:step=  424 loss=0.0037387 acc=0.9997559
 INFO:__main__:step=  425 loss=0.0039780 acc=0.9998779
 INFO:__main__:step=  426 loss=0.0041521 acc=0.9998779
 INFO:__main__:step=  427 loss=0.0044029 acc=0.9996948
 INFO:__main__:step=  428 loss=0.0037069 acc=0.9996948
 INFO:__main__:step=  429 loss=0.0039751 acc=0.9997559
 INFO:__main__:step=  430 loss=0.0036961 acc=0.9999390
 INFO:__main__:step=  431 loss=0.0041230 acc=0.9996948
 INFO:__main__:step=  432 loss=0.0037204 acc=0.9998169
 INFO:__main__:step=  433 loss=0.0041116 acc=0.9998169
 INFO:__main__:step=  434 loss=0.0043973 acc=0.9998779
 INFO:__main__:step=  435 loss=0.0042320 acc=0.9995728
 INFO:__main__:step=  436 loss=0.0048164 acc=0.9995728
 INFO:__main__:step=  437 loss=0.0038487 acc=0.9996948
 INFO:__main__:step=  438 loss=0.0044019 acc=0.9996948
 INFO:__main__:step=  439 loss=0.0036137 acc=0.9997559
 INFO:__main__:step=  440 loss=0.0040031 acc=0.9997559
 INFO:__main__:step=  441 loss=0.0037439 acc=1.0000000
 INFO:__main__:step=  442 loss=0.0047578 acc=0.9997559
 INFO:__main__:step=  443 loss=0.0037242 acc=1.0000000
 INFO:__main__:step=  444 loss=0.0035887 acc=0.9998169
 INFO:__main__:step=  445 loss=0.0040946 acc=0.9998779
 INFO:__main__:step=  446 loss=0.0038853 acc=0.9998779
 INFO:__main__:step=  447 loss=0.0045090 acc=0.9997559
 INFO:__main__:step=  448 loss=0.0035975 acc=0.9998779
 INFO:__main__:step=  449 loss=0.0039571 acc=0.9998169
 INFO:__main__:step=  450 loss=0.0033344 acc=0.9998779
 INFO:__main__:step=  451 loss=0.0038010 acc=0.9996338
 INFO:__main__:step=  452 loss=0.0037623 acc=0.9999390
 INFO:__main__:step=  453 loss=0.0039248 acc=0.9998779
 INFO:__main__:step=  454 loss=0.0038458 acc=0.9996948
 INFO:__main__:step=  455 loss=0.0038334 acc=0.9997559
 INFO:__main__:step=  456 loss=0.0039828 acc=0.9997559
 INFO:__main__:step=  457 loss=0.0037553 acc=0.9997559
 INFO:__main__:step=  458 loss=0.0034900 acc=0.9998169
 INFO:__main__:step=  459 loss=0.0039507 acc=0.9997559
 INFO:__main__:step=  460 loss=0.0040168 acc=0.9997559
 INFO:__main__:step=  461 loss=0.0039682 acc=0.9996338
 INFO:__main__:step=  462 loss=0.0033216 acc=1.0000000
 INFO:__main__:step=  463 loss=0.0035992 acc=0.9998779
 INFO:__main__:step=  464 loss=0.0035374 acc=0.9998169
 INFO:__main__:step=  465 loss=0.0039145 acc=0.9998169
 INFO:__main__:step=  466 loss=0.0036567 acc=0.9996338
 INFO:__main__:step=  467 loss=0.0035094 acc=0.9998169
 INFO:__main__:step=  468 loss=0.0038882 acc=0.9996338
 INFO:__main__:step=  469 loss=0.0037108 acc=0.9998169
 INFO:__main__:step=  470 loss=0.0034592 acc=0.9997559
 INFO:__main__:step=  471 loss=0.0031493 acc=0.9999390
 INFO:__main__:step=  472 loss=0.0038837 acc=0.9996948
 INFO:__main__:step=  473 loss=0.0035162 acc=0.9997559
 INFO:__main__:step=  474 loss=0.0037461 acc=0.9998169
 INFO:__main__:step=  475 loss=0.0037000 acc=1.0000000
 INFO:__main__:step=  476 loss=0.0042010 acc=0.9998169
 INFO:__main__:step=  477 loss=0.0034492 acc=0.9998779
 INFO:__main__:step=  478 loss=0.0035461 acc=1.0000000
 INFO:__main__:step=  479 loss=0.0035708 acc=0.9998169
 INFO:__main__:step=  480 loss=0.0040509 acc=0.9998169
 INFO:__main__:step=  481 loss=0.0033549 acc=0.9996948
 INFO:__main__:step=  482 loss=0.0035421 acc=1.0000000
 INFO:__main__:step=  483 loss=0.0032111 acc=0.9998779
 INFO:__main__:step=  484 loss=0.0039644 acc=0.9997559
 INFO:__main__:step=  485 loss=0.0042124 acc=0.9996338
 INFO:__main__:step=  486 loss=0.0040706 acc=0.9994507
 INFO:__main__:step=  487 loss=0.0035862 acc=0.9998169
 INFO:__main__:step=  488 loss=0.0044468 acc=0.9996338
 INFO:__main__:step=  489 loss=0.0036269 acc=0.9999390
 INFO:__main__:step=  490 loss=0.0031157 acc=0.9998169
 INFO:__main__:step=  491 loss=0.0037752 acc=0.9996948
 INFO:__main__:step=  492 loss=0.0033704 acc=0.9997559
 INFO:__main__:step=  493 loss=0.0040347 acc=0.9996948
 INFO:__main__:step=  494 loss=0.0034053 acc=0.9998169
 INFO:__main__:step=  495 loss=0.0036225 acc=0.9998779
 INFO:__main__:step=  496 loss=0.0042589 acc=0.9997559
 INFO:__main__:step=  497 loss=0.0040456 acc=0.9996338
 INFO:__main__:step=  498 loss=0.0035443 acc=0.9997559
 INFO:__main__:step=  499 loss=0.0035868 acc=1.0000000
 INFO:__main__:step=  500 loss=0.0035023 acc=0.9998779
 INFO:__main__:step= 1000 loss=0.0022645 acc=0.9996948
 INFO:__main__:step= 1500 loss=0.0006305 acc=0.9999390
 INFO:__main__:step= 2000 loss=0.0006203 acc=0.9997559
 INFO:__main__:step= 2500 loss=0.0004746 acc=0.9996948
 INFO:__main__:step= 3000 loss=0.0002501 acc=0.9999390
 INFO:__main__:step= 3500 loss=0.0001484 acc=1.0000000
 INFO:__main__:step= 4000 loss=0.0001385 acc=1.0000000
 INFO:__main__:step= 4500 loss=0.0001816 acc=0.9999390
 INFO:__main__:step= 5000 loss=0.0001943 acc=0.9999390
 INFO:__main__:step= 5500 loss=0.0001595 acc=1.0000000
 INFO:__main__:step= 6000 loss=0.0001608 acc=1.0000000
 INFO:__main__:step= 6500 loss=0.0000808 acc=1.0000000
 INFO:__main__:step= 7000 loss=0.0001676 acc=0.9998779
 INFO:__main__:step= 7500 loss=0.0001089 acc=1.0000000
 INFO:__main__:step= 8000 loss=0.0000847 acc=1.0000000
 INFO:__main__:step= 8500 loss=0.0000707 acc=1.0000000
 INFO:__main__:step= 9000 loss=0.0001036 acc=1.0000000
 INFO:__main__:step= 9500 loss=0.0001216 acc=1.0000000
 INFO:__main__:step=10000 loss=0.0000516 acc=1.0000000
 INFO:__main__:step=10500 loss=0.0000347 acc=1.0000000
 INFO:__main__:step=11000 loss=0.0000673 acc=1.0000000
 INFO:__main__:step=11500 loss=0.0000634 acc=1.0000000
 INFO:__main__:step=12000 loss=0.0000239 acc=1.0000000
 INFO:__main__:step=12500 loss=0.0000039 acc=1.0000000
 INFO:__main__:step=13000 loss=0.0000418 acc=1.0000000
 INFO:__main__:step=13500 loss=0.0000079 acc=1.0000000
 INFO:__main__:step=14000 loss=0.0000367 acc=1.0000000
 INFO:__main__:step=14500 loss=0.0000217 acc=1.0000000
 INFO:__main__:step=15000 loss=0.0000267 acc=1.0000000
 INFO:__main__:step=15500 loss=0.0000759 acc=1.0000000
 INFO:__main__:step=16000 loss=0.0000215 acc=1.0000000
 INFO:__main__:step=16500 loss=0.0000015 acc=1.0000000
 INFO:__main__:step=17000 loss=0.0000008 acc=1.0000000
 INFO:__main__:step=17500 loss=0.0000147 acc=1.0000000
 INFO:__main__:step=18000 loss=0.0000000 acc=1.0000000
 INFO:__main__:step=18500 loss=0.0000001 acc=1.0000000
 INFO:__main__:step=19000 loss=0.0000318 acc=1.0000000
 INFO:__main__:step=19500 loss=0.0000243 acc=1.0000000
 """
 pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
 rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
 df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)
 # Avoid log(0) issues for loss plot by clamping at a tiny positive value
 eps = 1e-10
 df["loss_clamped"] = df["loss"].clip(lower=eps)
 # Plot 1: Loss
 plt.figure(figsize=(9, 4.8))
 plt.plot(df["step"], df["loss_clamped"])
 plt.yscale("log")
 plt.xlabel("Step")
 plt.ylabel("Loss (log scale)")
 plt.title("Training Loss vs Step")
 plt.tight_layout()
 plt.show()
 # Plot 2: Accuracy
 df["err"] = (1.0 - df["acc"]).clip(lower=eps)
 plt.figure(figsize=(9, 4.8))
 plt.plot(df["step"], df["err"])
 plt.yscale("log")
 plt.xlabel("Step")
 plt.ylabel("Error rate (1 - accuracy) (log scale)")
 plt.title("Training Error Rate vs Step")
 plt.tight_layout()
 plt.show()
--- a/pairwise_comp_nn.py
+++ b/pairwise_comp_nn.py
@@ -0,0 +1,37 @@
 import torch
 from torch import nn
 # 2) Number "embedding" network: R -> R^d
 class NumberEmbedder(nn.Module):
    def __init__(self, d=2, hidden=4):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(1, hidden),
            nn.ReLU(),
            nn.Linear(hidden, d),
        )
    def forward(self, x):
        return self.net(x)
 # MLP Comparator head: takes (ea, eb, e) -> logit for "a > b"
 class PairwiseComparator(nn.Module):
    def __init__(self, d=2, hidden=4, k=0.5):
        super().__init__()
        self.log_k = nn.Parameter(torch.tensor([k]))
        self.embed = NumberEmbedder(d, hidden)
        self.head = nn.Sequential(
            nn.Linear(d, hidden),
            nn.ReLU(),
            nn.Linear(hidden, hidden),
            nn.ReLU(),
            nn.Linear(hidden, 1),
        )
    def forward(self, a, b):
        # trying to force antisym here: h(a,b)=-h(b,a)
        phi = self.head(self.embed(a-b))
        phi_neg = self.head(self.embed(b-a))
        logit = phi - phi_neg
        return (self.log_k ** 2) * logit
--- a/pairwise_compare.py
+++ b/pairwise_compare.py
@@ -2,23 +2,126 @@
 # pairwise_compare.py
 import logging
 import random
 import lzma
 import torch
 from torch import nn
 from torch.nn import functional as F
 import re
 import pandas as pd
 import matplotlib.pyplot as plt
 import pairwise_comp_nn as comp_nn
 # early pytorch device setup
 DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else "cpu"
 # Valves
-DIMENSIONS = 1
+DIMENSIONS = 2
-TRAIN_STEPS = 20000
+HIDDEN_NEURONS = 4
-TRAIN_BATCHSZ = 16384
+ADAMW_LR = 5e-3
-TRAIN_PROGRESS = 500
+ADAMW_DECAY = 5e-4
-BATCH_LOWER = -512.0
+TRAIN_STEPS = 2000
-BATCH_UPPER = 512.0
+TRAIN_BATCHSZ = 8192
-DO_VERBOSE_EARLY_TRAIN = True
+TRAIN_PROGRESS = 10
 BATCH_LOWER = -100.0
 BATCH_UPPER = 100.0
-def get_torch_info():
+# Files
 MODEL_PATH = "./files/pwcomp.model"
 LOGGING_PATH = "./files/output.log"
 EMBED_CHART_PATH = "./files/embedding_chart.png"
 EMBEDDINGS_DATA_PATH = "./files/embedding_data.csv"
 TRAINING_LOG_PATH = "./files/training.log.xz"
 LOSS_CHART_PATH = "./files/training_loss_v_step.png"
 ACC_CHART_PATH = "./files/training_error_v_step.png"
 # TODO: Move plotting into its own file
 def parse_training_log(file_path: str) -> pd.DataFrame:
    text: str = ""
    with lzma.open(file_path, mode='rt') as f:
        text = f.read()
    pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
    rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
    df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)
    # Avoid log(0) issues for loss plot by clamping at a tiny positive value
    eps = 1e-10
    df["loss_clamped"] = df["loss"].clip(lower=eps)
    return df
 # TODO: Move plotting into its own file
 def plt_loss_tstep(df: pd.DataFrame) -> None:
    # Plot 1: Loss
    plt.figure(figsize=(10, 6))
    plt.plot(df["step"], df["loss_clamped"])
    plt.yscale("log")
    plt.xlabel("Step")
    plt.ylabel("Loss (log scale)")
    plt.title("Training Loss vs Step")
    plt.tight_layout()
    plt.savefig(LOSS_CHART_PATH)
    plt.close()
    return None
 # TODO: Move plotting into its own file
 def plt_acc_tstep(df: pd.DataFrame, eps=1e-10) -> None:
    # Plot 2: Accuracy
    df["err"] = (1.0 - df["acc"]).clip(lower=eps)
    plt.figure(figsize=(10, 6))
    plt.plot(df["step"], df["err"])
    plt.yscale("log")
    plt.xlabel("Step")
    plt.ylabel("Error rate (1 - accuracy) (log scale)")
    plt.title("Training Error Rate vs Step")
    plt.tight_layout()
    plt.savefig(ACC_CHART_PATH)
    plt.close()
    return None
 # TODO: Move plotting into its own file
 def plt_embeddings(model: comp_nn.PairwiseComparator) -> None:
    import csv
    log.info("Starting embeddings sweep...")
    # samples for embedding mapping
    with torch.no_grad():
        xs = torch.arange(
            BATCH_LOWER,
            BATCH_UPPER + 1.0,
            0.1,
        ).unsqueeze(1).to(DEVICE)   # shape: (N, 1)
        embeddings = model.embed(xs)  # shape: (N, d)
    # move data back to CPU for plotting
    embeddings = embeddings.cpu()
    xs = xs.cpu()
    # Plot 3: x vs h(x)
    plt.figure(figsize=(10, 6))
    for i in range(embeddings.shape[1]):
        plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
    plt.title("x vs h(x)")
    plt.xlabel("x [input]")
    plt.ylabel("h(x) [embedding]")
    plt.legend()
    plt.savefig(EMBED_CHART_PATH)
    plt.close()
    # save all our embeddings data to csv
    csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
    with open(file=EMBEDDINGS_DATA_PATH, mode="w", newline='') as f:
        csv_file = csv.writer(f)
        csv_file.writerows(csv_data)
    return None
 def get_torch_info() -> None:
    log.info("PyTorch Version: %s", torch.__version__)
    log.info("HIP Version: %s", torch.version.hip)
    log.info("CUDA support: %s", torch.cuda.is_available())
@@ -28,67 +131,40 @@ def get_torch_info():
    log.info("Using %s compute mode", DEVICE)
-def set_seed(seed: int):
+def set_seed(seed: int) -> None:
    random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
-# 1) Data: pairs (a, b) with label y = 1 if a > b else 0
+# pairs (a, b) with label y = 1 if a > b else 0 -> (a,b,y)
-def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER):
+# uses epsi to select the window in which a == b for equality training
 def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER, epsi=1e-4) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    a = (high - low) * torch.rand(batch_size, 1) + low
    b = (high - low) * torch.rand(batch_size, 1) + low
-    # train for if a > b
+    epsi = 1e-4
-    y = (a > b).float()
+    y = torch.where(a > b + epsi, 1.0,
        torch.where(a < b - epsi, 0.0, 0.5))
    # removed but left for my notes; it seems training for equality hurts classifing results that are ~eq
    # when trained only on "if a > b => y", the model produces more accurate results when classifing if things are equal (~.5 prob).
    # eq = (a == b).float()
    # y = gt + 0.5 * eq
    return a, b, y
 # 2) Number "embedding" network: R -> R^d
 class NumberEmbedder(nn.Module):
    def __init__(self, d=8):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(1, 16),
            nn.ReLU(),
            nn.Linear(16, d),
        )
    def forward(self, x):
        return self.net(x)
 # 3) Comparator head: takes (ea, eb) -> logit for "a > b"
 class PairwiseComparator(nn.Module):
    def __init__(self, d=8):
        super().__init__()
        self.embed = NumberEmbedder(d)
        self.head = nn.Sequential(
            nn.Linear(2 * d + 1, 16),
            nn.ReLU(),
            nn.Linear(16, 1),
        )
    def forward(self, a, b):
        ea = self.embed(a)
        eb = self.embed(b)
        delta_ab = a - b
        x = torch.cat([ea, eb, delta_ab], dim=-1)
        return self.head(x)  # logits
 def training_entry():
    get_torch_info()
    # all prng seeds to 0 for deterministic outputs durring testing
    # the seed should initialized normally otherwise
    set_seed(0)
-    model = PairwiseComparator(d=DIMENSIONS).to(DEVICE)
+    model = comp_nn.PairwiseComparator(d=DIMENSIONS, hidden=HIDDEN_NEURONS).to(DEVICE)
-    opt = torch.optim.AdamW(model.parameters(), lr=2e-3)
+    opt = torch.optim.AdamW(model.parameters(), lr=ADAMW_LR, weight_decay=ADAMW_DECAY)
    log.info(f"Using {TRAINING_LOG_PATH} as the logging destination for training...")
    with lzma.open(TRAINING_LOG_PATH, mode='wt') as tlog:
        # training loop
        training_start_time = datetime.datetime.now()
        last_ack = datetime.datetime.now()
    # 4) Train
        for step in range(TRAIN_STEPS):
            a, b, y = sample_batch(TRAIN_BATCHSZ)
            a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
@@ -100,20 +176,24 @@ def training_entry():
            loss_fn.backward()
            opt.step()
        if step <= TRAIN_PROGRESS and DO_VERBOSE_EARLY_TRAIN is True:
            with torch.no_grad():
                pred = (torch.sigmoid(logits) > 0.5).float()
                acc = (pred == y).float().mean().item()
-            log.info(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}")
+            tlog.write(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}\n")
        elif step % TRAIN_PROGRESS == 0:
            with torch.no_grad():
                pred = (torch.sigmoid(logits) > 0.5).float()
                acc = (pred == y).float().mean().item()
            log.info(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}")
-    # 5) Quick test: evaluate accuracy on fresh pairs
+            # also print to normal text log occasionally to show some activity.
            # every 10 steps check if its been longer than 5 seconds since we've updated the user
            if step % 10 == 0:
                if (datetime.datetime.now() - last_ack).total_seconds() > 5:
                    log.info(f"still training... step={step} of {TRAIN_STEPS}")
                    last_ack = datetime.datetime.now()
    training_end_time = datetime.datetime.now()
    log.info(f"Training steps complete. Start time: {training_start_time} End time: {training_end_time}")
    # evaluate final model accuracy on fresh pairs
    with torch.no_grad():
-        a, b, y = sample_batch(TRAIN_BATCHSZ)
+        a, b, y = sample_batch(TRAIN_BATCHSZ*4)
        a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
        logits = model(a, b)
        pred = (torch.sigmoid(logits) > 0.5).float()
@@ -121,13 +201,15 @@ def training_entry():
        acc = (pred == y).float().mean().item()
    log.info(f"Final test acc: {acc} errors: {errors}")
-    # embed model depth into the model serialization
+    # embed model dimensions into the model serialization
-    torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS}, "model.pth")
+    torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS, "h": HIDDEN_NEURONS}, MODEL_PATH)
-    log.info("Saved PyTorch Model State to model.pth")
+    log.info(f"Saved PyTorch Model State to {MODEL_PATH}")
 def infer_entry():
-    model_ckpt = torch.load("model.pth", map_location=DEVICE)
+    get_torch_info()
-    model = PairwiseComparator(d=model_ckpt["d"]).to(DEVICE)
+
    model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
    model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
    model.load_state_dict(model_ckpt["state_dict"])
    model.eval()
@@ -138,44 +220,104 @@ def infer_entry():
    b = torch.tensor([[p[1]] for p in pairs], dtype=torch.float32, device=DEVICE)
    # sanity check before inference
-    log.info(f"a.device: {a.device} model.device: {next(model.parameters()).device}")
+    log.debug(f"a.device: {a.device} model.device: {next(model.parameters()).device}")
    with torch.no_grad():
        probs = torch.sigmoid(model(a, b))
    log.info(f"Output probabilities for {pairs.__len__()} pairs")
    for (x, y), p in zip(pairs, probs):
        log.info(f"P({x} > {y}) = {p.item():.3f}")
 def graphs_entry():
    get_torch_info()
    model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
    model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
    model.load_state_dict(model_ckpt["state_dict"])
    model.eval()
    plt_embeddings(model)
    data = parse_training_log(TRAINING_LOG_PATH)
    plt_loss_tstep(data)
    plt_acc_tstep(data)
 help_text = r"""
 pairwise_compare.py — tiny pairwise "a > b?" neural comparator
 USAGE
  python3 pairwise_compare.py train
    Train a PairwiseComparator on synthetic (a,b) pairs sampled uniformly from
    [BATCH_LOWER, BATCH_UPPER]. Labels are:
      1.0 if a > b + epsi
      0.0 if a < b - epsi
      0.5 otherwise (near-equality window)
    Writes training metrics to:
      ./files/training.log.xz
    Saves the trained model checkpoint to:
      ./files/pwcomp.model
  python3 pairwise_compare.py infer
    Load ./files/pwcomp.model and run inference on a built-in list of test pairs.
    Prints probabilities as:
      P(a > b) = sigmoid(model(a,b))
  python3 pairwise_compare.py graphs
    Load ./files/pwcomp.model and generate plots + exports:
      ./files/embedding_chart.png     (embed(x) vs x for each embedding dimension)
      ./files/embedding_data.csv      (x and embedding vectors)
      ./files/training_loss_v_step.png
      ./files/training_error_v_step.png  (1 - acc, log scale)
    Requires that ./files/training.log.xz exists (i.e., you ran "train" first).
 FILES
  ./files/output.log          General runtime log (info/errors)
  ./files/pwcomp.model        Torch checkpoint: {"state_dict": ..., "d": DIMENSIONS}
  ./files/training.log.xz     step/loss/acc trace used for plots
 NOTES
  - DEVICE is chosen via torch.accelerator if available, else CPU.
  - Hyperparameters are controlled by the "Valves" constants near the top.
 """
 if __name__ == '__main__':
    import sys
    import os
    import datetime
    # TODO: tidy up the paths to files and checking if the directory exists
    if not os.path.exists("./files/"):
        os.mkdir("./files")
    logging.basicConfig(level=logging.INFO,
                        format='%(asctime)s - %(levelname)s - %(message)s',
                        handlers=[
                            logging.FileHandler(LOGGING_PATH),
                            logging.StreamHandler(stream=sys.stdout)
                        ])
    log = logging.getLogger(__name__)
-    logging.basicConfig(filename='pairwise_compare.log', level=logging.INFO)
+    log.info(f"Log file {LOGGING_PATH} opened {datetime.datetime.now()}")
    log.info(f"Log opened {datetime.datetime.now()}")
    get_torch_info()
    name = os.path.basename(sys.argv[0])
    if name == 'train.py':
        training_entry()
    elif name == 'infer.py':
        infer_entry()
    else:
        # alt call patern
    # python3 pairwise_compare.py train
    # python3 pairwise_compare.py infer
    # python3 pairwise_compare.py graphs
    if len(sys.argv) > 1:
-            mode = sys.argv[1].strip().lower()
+        match sys.argv[1].strip().lower():
-            if mode == "train":
+            case "train":
                training_entry()
-            elif mode == "infer":
+            case "infer":
                infer_entry()
-            else:
+            case "graphs":
                graphs_entry()
            case "help":
                log.info(help_text)
            case mode:
                log.error(f"Unknown operation: {mode}")
-                log.error("Invalid call syntax, call script as \"train.py\" or \"infer.py\" or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")
+                log.error("valid options are one of [\"train\", \"infer\", \"graphs\", \"help\"]")
-        else:
+                log.info(help_text)
            log.error("Not enough arguments passed to script; call as train.py or infer.py or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")
    log.info(f"Log closed {datetime.datetime.now()}")
--- a/train.py
+++ b/train.py
@@ -1 +0,0 @@
 pairwise_compare.py
Author	SHA1	Message	Date
Elaina Claus	9af36e7145	man python stinks...	2025-12-25 14:54:08 +00:00
Elaina Claus	23fddbe5b9	updated comment for PairwiseComparator	2025-12-25 14:53:35 +00:00
Elaina Claus	acbccebb2c	renamed ADAMW_WiDECAY	2025-12-25 14:48:07 +00:00
Elaina Claus	edf8d46123	wut	2025-12-25 02:13:23 +00:00
Elaina Claus	4f500e8b4c	write all data on training to training log	2025-12-25 02:07:49 +00:00
Elaina Claus	921e24b451	embeded # of hidden neurons in model save data added more valves for options. will update the console every 5 seconds when training now	2025-12-25 01:52:19 +00:00
Elaina Claus	d46712ff53	make the defaults more sane for the task its still overkill	2025-12-25 01:23:54 +00:00
Elaina Claus	cd72cd7052	changes to figure sizes and add labels to embedding chart	2025-12-23 13:44:38 -05:00
Elaina Claus	755161c152	unused var	2025-12-23 13:29:52 -05:00
Elaina Claus	1d70935b64	general clean up & added help text removed symbolic link calling path	2025-12-23 13:06:07 -05:00
Elaina Claus	0e2098ceec	remove linked files	2025-12-23 12:30:18 -05:00
Elaina Claus	cfef24921d	moved output_graphs into main script and restructured training log is seperate from normal output & is compressed slightly adjusted lr made final test stage test 4xTRAIN_BATCHSZ number of samples	2025-12-23 10:18:02 -05:00
Elaina Claus	9ea8ef3458	default k=0.5	2025-12-23 10:14:08 -05:00
Elaina Claus	6e31865a84	added episilon for equality check major layout changes in the network	2025-12-22 21:47:16 -05:00
Elaina Claus	997303028e	output plots and csv data for e(x) over the trained range.	2025-12-22 12:23:59 -05:00
Elaina Claus	c3fbc44a34	add cpu pytorch instructions	2025-12-20 01:59:50 +00:00
Elaina Claus	5e5ad1bc20	fix log entry for model state saving adjust default batch size and passes to be a bit more CPU pytorch friendly	2025-12-20 01:54:42 +00:00
Elaina Claus	0d6a92823a	all files (logs/models/images) go into ./files using AdamW optim again, AdamW is the go to for small toys and transformers. refactored NN classes to thier own module under pairwise_comp_nn.py	2025-12-19 09:15:41 -05:00
Elaina Claus	cfcec07b9c	updated .gitignore	2025-12-19 09:13:07 -05:00
Elaina Claus	bf15e7b6c8	save the plots for viewing	2025-12-18 22:24:23 -05:00
Elaina Claus	0385b0acc8	testing adadelta optimizer	2025-12-18 22:24:01 -05:00