Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography
This repository contains the code used to run the active learning experiments detailed in our paper.
In order to run our code, you'll need these main packages:
- Python>=3.5
- Numpy>=1.14.3
- Scipy>=1.0.0
- TensorFlow==1.13.1
- Keras==2.2.4
The code is run using the main.py file in the following way:
python main.py --lambda_e xxx --classes xxx --batch_size xxx --initial_size xxx --iterations xxx --gpu xxx
- lambda_e: a hyperparameter that balances entropy and discriminative scores.
- classes: the number of classes for classification.
- batch_size: the size of the batch of examples to be labeled in every iteration.
- initial_size: the amount of labeled examples to start the experiment with (chosen randomly).
- iterations: the amount of active learning iterations to run in the experiment.