{"id":25460921,"url":"https://github.com/uefi-code/neuro_mykakuritsu","last_synced_at":"2026-05-19T05:39:45.941Z","repository":{"id":133003360,"uuid":"527365964","full_name":"UEFI-code/Neuro_myKakuritsu","owner":"UEFI-code","description":"Bionic Neuro Activation Pattern Research","archived":false,"fork":false,"pushed_at":"2023-12-28T10:06:31.000Z","size":46450,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-04-14T23:19:45.218Z","etag":null,"topics":["bionic","deep-learning","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/UEFI-code.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2022-08-22T00:56:45.000Z","updated_at":"2022-09-01T02:04:46.000Z","dependencies_parsed_at":"2023-12-28T11:36:41.687Z","dependency_job_id":null,"html_url":"https://github.com/UEFI-code/Neuro_myKakuritsu","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UEFI-code%2FNeuro_myKakuritsu","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UEFI-code%2FNeuro_myKakuritsu/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UEFI-code%2FNeuro_myKakuritsu/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UEFI-code%2FNeuro_myKakuritsu/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/UEFI-code","download_url":"https://codeload.github.com/UEFI-code/Neuro_myKakuritsu/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239412468,"owners_count":19634016,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bionic","deep-learning","pytorch"],"created_at":"2025-02-18T05:05:22.558Z","updated_at":"2025-10-20T10:43:05.884Z","avatar_url":"https://github.com/UEFI-code.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neuro myKakuritsu Research Code with PyTorch\n\n## Idea\n\nHow your brain kept your memory while neuro cells are deactivating - or just there Dendritic deactivating?\n\nCurrently experiment on ImageNet 2012 showed our benefits, especially keep p = 50% in evaluation our performance 7.5% improvement than Normal way at Acc1, and 1.6% improvement at Acc5! That reflected our method improved the neuro cells random cooperation or de-dependence ability.\n\nTo see experiment details, go to [The Archieve](/Archieve) Page.\n\nStill Need more Experment to prove this guess.\n\n## Pretrained Data\n\nWe will no longer upload LFS because we have no money to buy the quato.\n\nHowever, we will try to upload the Archieved pth files to [Google Drive](https://drive.google.com/drive/folders/1J2_FkFKFnkagXT4x3rEZagRy-eK4HX8w?usp=sharing).\n\n## Usage\n\nThere are two version of experiment code.\n\nImagenet\\_TrainFromZero.py contains NO pretrained ResNet152's weight, keeps its convolutional layers and removed its linear for experiment.\n\nImagenet\\_TrainForExp.py keeps the pretrained ResNet152's weight, and model structure same as above.\n\n```bash\npython3 Imagenet_TrainForYOULIKE.py [args] [Dataset_Dir]\n\npositional arguments:\n  DIR                   path to dataset (default: imagenet)\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -a ARCH, --arch ARCH  model architecture: Kakuritsu and Dropout, with ResNet152\n  -j N, --workers N     number of data loading workers (default: 4)\n  --epochs N            number of total epochs to run\n  --start-epoch N       manual epoch number (useful on restarts)\n  -b N, --batch-size N  mini-batch size (default: 64), this is the total batch size of all GPUs on the current node when using Data Parallel or Distributed Data Parallel\n  --lr LR, --learning-rate LR\n                        initial learning rate\n  --momentum M          momentum\n  --wd W, --weight-decay W\n                        weight decay (default: 1e-4)\n  -p N, --print-freq N  print frequency (default: 10)\n  --resume PATH         path to latest checkpoint (default: none)\n  -e, --evaluate        evaluate model on validation set\n  -sw, --switch         Switch Dropout or myKakuritsu during Validation\n  --pretrained          use pre-trained model\n  --world-size WORLD_SIZE\n                        number of nodes for distributed training\n  --rank RANK           node rank for distributed training\n  --dist-url DIST_URL   url used to set up distributed training\n  --dist-backend DIST_BACKEND\n                        distributed backend\n  --seed SEED           seed for initializing training.\n  --gpu GPU             GPU id to use.\n  --multiprocessing-distributed\n                        Use multi-processing distributed training to launch N processes per node, which has N GPUs. This is the fastest way to use PyTorch for either single\n                        node or multi node data parallel training\n  --dummy               use fake data to benchmark\n```\n\n## Archieve\n\nArchieved code, pth files, experiment results can be found [here](Archieve/)\n\n## Credit\n\nSuperHacker UEFI (Shizhuo Zhang)\n\nCookie (Yue Fang)\n\nResearch supported by Automation School, BISTU; Microsoft The Practice Space (ai-edu)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuefi-code%2Fneuro_mykakuritsu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fuefi-code%2Fneuro_mykakuritsu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fuefi-code%2Fneuro_mykakuritsu/lists"}