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.

Dependencies

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

Running the Code

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.