mirror of
https://github.com/jung-geun/PSO.git
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jung-geun
813433000a
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
12 lines
2.1 KiB
CSV
12 lines
2.1 KiB
CSV
profile,dataset,method,n_particles,epochs,n_seeds,eval_acc_mean,eval_acc_std,eval_acc_median,eval_acc_iqr,eval_acc_ci95,eval_loss_mean,eval_loss_std,eval_loss_median,eval_loss_iqr,eval_loss_ci95,eval_mse_mean,eval_mse_std,train_acc_mean,train_loss_mean,runtime_seconds_mean,runtime_seconds_std,rank_acc,rank_loss
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adaptive_moment_.10,MNIST,adaptive_moment,30,80,5,0.63,0.018276,0.641,0.027,0.022692,1.243339,0.102561,1.253713,0.068197,0.127344,0.051633,0.002578,0.7099,0.978244,4.286662,0.289397,2,5
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adaptive_moment_.25,MNIST,adaptive_moment,30,80,5,0.5632,0.02938,0.569,0.034,0.03648,1.499979,0.119449,1.483628,0.043413,0.148313,0.06119,0.003677,0.6379,1.228023,3.290951,0.425997,8,7
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adaptive_moment_.50,MNIST,adaptive_moment,30,80,5,0.5186,0.039087,0.51,0.019,0.048532,1.683781,0.121571,1.683062,0.08627,0.150948,0.067331,0.003822,0.5705,1.4637,3.202502,0.269844,9,9
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inertia_canonical,MNIST,inertia,30,80,5,0.4676,0.024358,0.472,0.033,0.030244,1.720983,0.118092,1.699598,0.020267,0.146628,0.070356,0.002531,0.5277,1.528197,3.119898,0.30594,10,10
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inertia_tuned,MNIST,inertia,30,80,5,0.6162,0.05131,0.635,0.007,0.063709,1.2366,0.124281,1.194831,0.029657,0.154312,0.05278,0.005923,0.6931,0.999514,3.131882,0.408534,4,3
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tuned_adam_100_lr.01,MNIST,inertia,30,80,5,0.8558,0.002387,0.856,0.003,0.002964,0.470271,0.011931,0.472233,0.019931,0.014814,0.02164,0.000368,0.9158,0.298524,4.990688,0.434352,1,1
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tuned_full_evaluation,MNIST,inertia,30,80,5,0.6254,0.02805,0.624,0.029,0.034828,1.179009,0.050059,1.194071,0.052015,0.062156,0.051537,0.00241,0.687867,1.00514,3.377083,0.469302,3,2
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tuned_no_mutation,MNIST,inertia,30,80,5,0.6042,0.030376,0.607,0.003,0.037716,1.249836,0.084985,1.215945,0.022012,0.105521,0.053483,0.003055,0.6686,1.074396,3.347668,0.3431,6,6
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tuned_particle_reset,MNIST,inertia,30,80,5,0.6162,0.05131,0.635,0.007,0.063709,1.2366,0.124281,1.194831,0.029657,0.154312,0.05278,0.005923,0.6931,0.999514,4.176388,0.449217,5,4
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tuned_uniform_initialization,MNIST,inertia,30,80,5,0.5698,0.023952,0.575,0.017,0.02974,1.619196,0.157329,1.641514,0.149905,0.195347,0.061757,0.0042,0.616,1.345857,4.306866,0.442651,7,8
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