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MiniBatchSVM\nMini-batch `SVM` / `Logistic Regresion`, Online learning for large scale data. \n\n## Usage\n\n```\n./MiniBatchSVM.py \n```\nBy default: run mnist with `SVM`, batchsize=1000, epoch=20.\n```\n./MiniBatchSVM.py --model log\n```\nBy default: run mnist with `Logistic Regresion`, batchsize=1000, epoch=20.\n\nYou can train your own data by specific the options\n\n```\n./MiniBatchSVM.py [options] [--trianlist path-to-training-data-list-file] \n```\n\n## Options\n```\n  -h, --help            show this help message and exit\n  -m MODEL, --model=MODEL\n                        svm, log\n  -t TRAINLIST, --trainlist=TRAINLIST\n                        trainlist file\n  -T TESTLIST, --testlist=TESTLIST\n                        testlist file\n  -b BATCHSIZE, --batchsize=BATCHSIZE\n                        batch size\n  -e EPOCH, --epoch=EPOCH\n                        max epoch\n  -c NCLASSES, --nclasses=NCLASSES\n                        num of the class\n  -n, --norm            do mean normalization\n  -l, --labelstart1     use this option when  the label of your data is bengin\n                        at 1\n                        \n```\n\n## Example\n\n    $ ./MiniBatchSVM.py  \n```\nUsing Hinge Loss SVM...\nEpoch 1/20\n   Test Score 0.8463 BestScore 0.8463\nEpoch 2/20\n   Test Score 0.8507 BestScore 0.8507\nEpoch 3/20\n   Test Score 0.8707 BestScore 0.8707\nEpoch 4/20\n   Test Score 0.8441 BestScore 0.8707\nEpoch 5/20\n   Test Score 0.8636 BestScore 0.8707\nEpoch 6/20\n   Test Score 0.8493 BestScore 0.8707\nEpoch 7/20\n   Test Score 0.8541 BestScore 0.8707\nEpoch 8/20\n   Test Score 0.8134 BestScore 0.8707\nEpoch 9/20\n   Test Score 0.8756 BestScore 0.8756\nEpoch 10/20\n   Test Score 0.8282 BestScore 0.8756\nEpoch 11/20\n   Test Score 0.8607 BestScore 0.8756\nEpoch 12/20\n   Test Score 0.8482 BestScore 0.8756\nEpoch 13/20\n   Test Score 0.8442 BestScore 0.8756\nEpoch 14/20\n   Test Score 0.8599 BestScore 0.8756\nEpoch 15/20\n   Test Score 0.8737 BestScore 0.8756\nEpoch 16/20\n   Test Score 0.8228 BestScore 0.8756\nEpoch 17/20\n   Test Score 0.8561 BestScore 0.8756\nEpoch 18/20\n   Test Score 0.8688 BestScore 0.8756\nEpoch 19/20\n   Test Score 0.8635 BestScore 0.8756\nFinished, The finally best score is: 0.8756\n```\n\n\n## Logistic Regresion\n\n## SGD\n\n## Mini-batch\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkuixu%2Fminibatchsvm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkuixu%2Fminibatchsvm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkuixu%2Fminibatchsvm/lists"}