NOTE: An up-to-date version of this repository is at https://github.com/Jeffrey-Ede/adaptive-scans
This repository is for the preprint|paper "Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning". It contains TensorFlow code to train recurrent actors to cooperate with a convolutional generator to complete partial scans.
Examples show test set 1/23.04 px coverage adaptive partial scans, target outputs and generated partial scan completions for 96x96 crops from STEM images.
To continue training the neural network; from scratch or to fine-tune it, you will need to adjust some of the variables at the top of train.py files. Specifically, variables indicating the location of your datasets and locations to save logs and checkpoints to. Note that there may be minor differences between the script and the paper due to active development. Add empty __init__.py files to dnc subdirectories to run code.
Checkpoints for fully trained models are here. They are for experiments 125 (LSTM), 126 (Spirals), and 127 (DNC) after 500k and 1000k training iterations. To load the models, change the save location in the checkpoint file to your save location.
Datasets containing 19769 STEM images cropped or downsampled to 96x96 are here. Other dataset variants are also available.
Our repository contains support vector graphiscs and python scripts used to create figures. In addition, read_loss_log.py is helpful to read loss logs output during training.
Jeffrey Ede: j.m.ede@warwick.ac.uk