开源软件名称（OpenSource Name）：

开源软件地址(OpenSource Url)：

开源编程语言(OpenSource Language)：

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.

2. 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.

3. 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.

4. 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/.

5. Display the results of the hyperparameters search in a nice format

python synthesize_results.py --parent_dir experiments/learning_rate

6. 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

• CS230 Deep Learning
• Paper