• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    迪恩网络公众号

ThanhTunggggg/DeepLoc: Predicting protein subcellular localization using deep le ...

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

ThanhTunggggg/DeepLoc

开源软件地址(OpenSource Url):

https://github.com/ThanhTunggggg/DeepLoc

开源编程语言(OpenSource Language):

Python 100.0%

开源软件介绍(OpenSource Introduction):

DeepLoc

Predicting protein subcellular localization using deep learning

Requirements

virtualenv -p python3 .env
source .env/bin/activate
pip install -r requirements.txt

  • Download dataset. Then, move it to 'data' folder.

Steps

  1. Build the dataset Run the following script
python build_dataset.py

It will extract the sentences and classes from the dataset, split it into train/val/test and save it in a convenient format for model.

  1. Build vocabularies and parameters for dataset by running
python build_vocab.py --data_dir data/

It will write vocabulary files chars.txt and classes.txt containing the amino acid notations and classes in the dataset. It will also save a dataset_params.json with some extra information.

  1. Train Simply run
python train.py --data_dir data --model_dir experiments/base_model

It will instantiate a model and train it on the training set following the hyperparameters specified in params.json. It will also evaluate some metrics on the development set.

  1. First hyperparameters search Created a new directory learning_rate in experiments. Now, run
python search_hyperparams.py --data_dir data --parent_dir experiments/learning_rate

It will train and evaluate a model with different values of learning rate defined in search_hyperparams.py and create a new directory for each experiment under experiments/learning_rate/.

  1. Display the results of the hyperparameters search in a nice format
python synthesize_results.py --parent_dir experiments/learning_rate
  1. Evaluation on the test set Run many experiments and selected best model and hyperparameters based on the performance on the development set,finally evaluate the performance of model on the test set. Run
python evaluate.py --data_dir data --model_dir experiments/base_model

References




鲜花

握手

雷人

路过

鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
热门推荐
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服(服务时间 9:00~18:00)

在线QQ客服
地址:深圳市南山区西丽大学城创智工业园
电邮:jeky_zhao#qq.com
移动电话:139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap