arc-pytorch
PyTorch implementation of Attentive Recurrent Comparators by Shyam et al.
A blog explaining Attentive Recurrent Comparators
Visualizing Attention
On Same characters
On Different Characters
How to run?
Download data
python download_data.py
A one-time 52MB download. Shouldn’t take more than a few minutes.
Train
python train.py --cuda
Let it train until the accuracy rises to at least 80%. Early stopping is not implemented yet. You will have to manually kill the process.
Visualize
python viz.py --cuda --load 0.13591022789478302 --same
Run with exactly the same parameters as train.py and specify the model to load. Specify “–same” if you want to generate a sample with same characters in both images. The script dumps images to a directory in visualization. The name of directory is taken from –name parameter if specified, else name is a function of the parameters of network.
