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Build Status MIT

Implementation of the DRAW network architecture

This repository contains a reimplementation of the Deep Recurrent Attentive Writer (DRAW) network architecture introduced by K. Gregor, I. Danihelka, A. Graves and D. Wierstra. The original paper can be found at

http://arxiv.org/pdf/1502.04623

animation.gif

Dependencies

Draw currently works with the "cutting-edge development version". But since the API is subject to change, you might consider installing this known to be supported version:

You also need to install

Data

You need to set the location of your data directory:

export FUEL_DATA_PATH=/home/user/data

fuel-download and fuel-convert are used to obtain and convert training datasets. E.g. for binarized MNIST

cd $FUEL_DATA_PATH
fuel-download binarized_mnist
fuel-convert binarized_mnist

or similarly for SVHN

cd $FUEL_DATA_PATH
fuel-download svhn -d . 2
fuel-convert svhn -d . 2

Training with attention

To train a model with a 2x2 read and a 5x5 write attention window run

cd draw
./train-draw.py --dataset=bmnist --attention=2,5 --niter=64 --lr=3e-4 --epochs=100

On Amazon g2xlarge it takes more than 40min for Theano's compilation to end and training to start. If you enable the bokeh-server, once training starts you can track its live plotting. It will take about 2 days to train the model.

After each epoch it will save the following files:

  • a pickle of the model
  • a pickle of the log
  • sampled output image for that epoch
  • animation of sampled output

Generating animations

To generate sampled output including an animation run

python sample.py svhn_model.pkl --channels 3 --size 32

Note that in order to load a model and to generate samples all dependencies are needed. This unfortunately also this includes the GPU because python cannot unpickle CudaNdarray objects without it. This is a known problem that we don't yet a have general solution to.

SVHN

To train a model on SVHN

python train-draw.py --name=my_svhn --dataset=svhn2 \
  --attention=5,5 --niter=32 --lr=3e-4 --epochs=100 \
  --enc-dim 512 --dec-dim 512

After 100-200 epochs, the model above achieved a test_nll_bound of 1825.82.

Log

Run

python plot-kl.py [pickle-of-log]

to create a visualization of the KL divergence potted over inference iterations and epochs. E.g:

KL-Divergenc

Testing

Run

nosetests -v tests

to execute the testsuite. Run

cd draw
./attention.py

to test the attention windowing code on some image. It will open three windows: A window displaying the original input image, a window displaying some extracted, downsampled content (testing the read-operation), and a window showing the upsampled content (matching the input size) after the write operation.