Implemenation of MADE: Masked Autoencoder for Distribution Estimation with Chainer. This repository's aim is to adapt A. Karpathy's MADE codes from PyTorch to chainer. I newly implemented sampling function.
This is the blog post (only in Japanese) about this repository.
You can also find the author's original code here.
MIT license. Contributions welcome.
python 2.x, chainer 4.3.1, numpy, matplotlib, and binarized mnist dataset.
Training a 1-layer MLP of 500 units with only a single mask, and using a single fixed (but random) ordering as so:
python run.py --data-path binarized_mnist.npz -q 500
which converges at binary cross entropy loss of 94.06.
We can use 10 orderings (-n 10) and also average over the 10 at inference time (-s 10):
python run.py --data-path binarized_mnist.npz -q 500 -n 10 -s 10
which gives a much better test loss of 83.08.