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feat: modernize PSO and add convergence research

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Migrate the package and examples to the tensor-native PyTorch implementation, add benchmark evidence, and add the guarded post-training convergence protocol with TensorBoard progress monitoring and hash-verified recovery.

Constraint: Preserve one-shot official-test sealing and auditable research artifacts

Rejected: Commit local .omc runs and downloaded datasets | multi-gigabyte runtime state is machine-local

Confidence: high

Scope-risk: broad

Not-tested: Production CUDA run on pieroot-server
This commit is contained in:
jung-geun
2026-09-07 22:03:25 +09:00
parent 9745bb7ad4
commit 813433000a
120 changed files with 198148 additions and 4745 deletions
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benchmark_results/pso_v4_deep_accuracy.csv
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lane,profile_or_arch,seed,model_name,param_count,initial_test_acc,final_test_acc,final_test_loss,subset_fitness_acc,subset_fitness_loss,pso_epochs,adam_epochs,elapsed_sec,model_fingerprint,data_fingerprint
architecture,raw_linear,101,Raw Linear (784->10),7850,0.1449,0.9239,0.269691,,,0,10,2.6001,12cef21a80b85b0b,8dd702555745641a
architecture,raw_linear,102,Raw Linear (784->10),7850,0.1308,0.924,0.270518,,,0,10,2.6265,0f099d5ae79e7863,8dd702555745641a
architecture,raw_linear,103,Raw Linear (784->10),7850,0.0559,0.9257,0.266626,,,0,10,2.8535,23a4a6362abdad3d,8dd702555745641a
architecture,raw_mlp,101,Raw MLP (784->128->64->10),109386,0.0812,0.9773,0.076661,,,0,10,8.8041,9ac96d5f45d71cb3,8dd702555745641a
architecture,raw_mlp,102,Raw MLP (784->128->64->10),109386,0.0522,0.976,0.081621,,,0,10,5.7141,55966246db4dfff1,8dd702555745641a
architecture,raw_mlp,103,Raw MLP (784->128->64->10),109386,0.1111,0.9776,0.076823,,,0,10,4.5491,723217e2418edcfd,8dd702555745641a
architecture,compact_cnn,101,"Compact CNN (9,098 params)",9098,0.0851,0.9866,0.039444,,,0,10,6.228,db41fb515dcb49fa,8dd702555745641a
architecture,compact_cnn,102,"Compact CNN (9,098 params)",9098,0.0963,0.9858,0.042864,,,0,10,5.396,efd743cad60c7530,8dd702555745641a
architecture,compact_cnn,103,"Compact CNN (9,098 params)",9098,0.1072,0.9836,0.04912,,,0,10,5.4935,17891ec08e79ee74,8dd702555745641a
optimizer,adam_only,101,Compact CNN (Adam-Only),9098,0.0851,0.9866,0.039444,,,0,10,6.228,db41fb515dcb49fa,8dd702555745641a
optimizer,adam_only,102,Compact CNN (Adam-Only),9098,0.0963,0.9858,0.042864,,,0,10,5.396,efd743cad60c7530,8dd702555745641a
optimizer,adam_only,103,Compact CNN (Adam-Only),9098,0.1072,0.9836,0.04912,,,0,10,5.4935,17891ec08e79ee74,8dd702555745641a
optimizer,pso_only,101,Compact CNN (PSO-Only),9098,0.0851,0.3901,22.182518,0.399,21.674469,40,0,2.5761,db41fb515dcb49fa,8dd702555745641a
optimizer,pso_only,102,Compact CNN (PSO-Only),9098,0.0963,0.3886,6.031698,0.3925,5.977414,40,0,2.331,efd743cad60c7530,8dd702555745641a
optimizer,pso_only,103,Compact CNN (PSO-Only),9098,0.1072,0.3242,14.099036,0.3405,14.065619,40,0,2.7469,17891ec08e79ee74,8dd702555745641a
optimizer,hybrid,101,Compact CNN (Hybrid),9098,0.0851,0.9658,0.109308,0.399,21.674469,40,10,7.4414,db41fb515dcb49fa,8dd702555745641a
optimizer,hybrid,102,Compact CNN (Hybrid),9098,0.0963,0.9807,0.061528,0.3925,5.977414,40,10,7.226,efd743cad60c7530,8dd702555745641a
optimizer,hybrid,103,Compact CNN (Hybrid),9098,0.1072,0.9725,0.088789,0.3405,14.065619,40,10,7.7426,17891ec08e79ee74,8dd702555745641a
1 lane profile_or_arch seed model_name param_count initial_test_acc final_test_acc final_test_loss subset_fitness_acc subset_fitness_loss pso_epochs adam_epochs elapsed_sec model_fingerprint data_fingerprint
2 architecture raw_linear 101 Raw Linear (784->10) 7850 0.1449 0.9239 0.269691 0 10 2.6001 12cef21a80b85b0b 8dd702555745641a
3 architecture raw_linear 102 Raw Linear (784->10) 7850 0.1308 0.924 0.270518 0 10 2.6265 0f099d5ae79e7863 8dd702555745641a
4 architecture raw_linear 103 Raw Linear (784->10) 7850 0.0559 0.9257 0.266626 0 10 2.8535 23a4a6362abdad3d 8dd702555745641a
5 architecture raw_mlp 101 Raw MLP (784->128->64->10) 109386 0.0812 0.9773 0.076661 0 10 8.8041 9ac96d5f45d71cb3 8dd702555745641a
6 architecture raw_mlp 102 Raw MLP (784->128->64->10) 109386 0.0522 0.976 0.081621 0 10 5.7141 55966246db4dfff1 8dd702555745641a
7 architecture raw_mlp 103 Raw MLP (784->128->64->10) 109386 0.1111 0.9776 0.076823 0 10 4.5491 723217e2418edcfd 8dd702555745641a
8 architecture compact_cnn 101 Compact CNN (9,098 params) 9098 0.0851 0.9866 0.039444 0 10 6.228 db41fb515dcb49fa 8dd702555745641a
9 architecture compact_cnn 102 Compact CNN (9,098 params) 9098 0.0963 0.9858 0.042864 0 10 5.396 efd743cad60c7530 8dd702555745641a
10 architecture compact_cnn 103 Compact CNN (9,098 params) 9098 0.1072 0.9836 0.04912 0 10 5.4935 17891ec08e79ee74 8dd702555745641a
11 optimizer adam_only 101 Compact CNN (Adam-Only) 9098 0.0851 0.9866 0.039444 0 10 6.228 db41fb515dcb49fa 8dd702555745641a
12 optimizer adam_only 102 Compact CNN (Adam-Only) 9098 0.0963 0.9858 0.042864 0 10 5.396 efd743cad60c7530 8dd702555745641a
13 optimizer adam_only 103 Compact CNN (Adam-Only) 9098 0.1072 0.9836 0.04912 0 10 5.4935 17891ec08e79ee74 8dd702555745641a
14 optimizer pso_only 101 Compact CNN (PSO-Only) 9098 0.0851 0.3901 22.182518 0.399 21.674469 40 0 2.5761 db41fb515dcb49fa 8dd702555745641a
15 optimizer pso_only 102 Compact CNN (PSO-Only) 9098 0.0963 0.3886 6.031698 0.3925 5.977414 40 0 2.331 efd743cad60c7530 8dd702555745641a
16 optimizer pso_only 103 Compact CNN (PSO-Only) 9098 0.1072 0.3242 14.099036 0.3405 14.065619 40 0 2.7469 17891ec08e79ee74 8dd702555745641a
17 optimizer hybrid 101 Compact CNN (Hybrid) 9098 0.0851 0.9658 0.109308 0.399 21.674469 40 10 7.4414 db41fb515dcb49fa 8dd702555745641a
18 optimizer hybrid 102 Compact CNN (Hybrid) 9098 0.0963 0.9807 0.061528 0.3925 5.977414 40 10 7.226 efd743cad60c7530 8dd702555745641a
19 optimizer hybrid 103 Compact CNN (Hybrid) 9098 0.1072 0.9725 0.088789 0.3405 14.065619 40 10 7.7426 17891ec08e79ee74 8dd702555745641a