thrillington 0.0.2a1

Replication of "Recurrent models of visual attention", Mnih et al. 2014

Recurrent Models of Visual Attention

Replication in Tensorflow of the following paper:
Mnih, Volodymyr, Nicolas Heess, and Alex Graves.
"Recurrent models of visual attention."
Advances in neural information processing systems. 2014.
https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention

Based in part on the following implementations:

• https://github.com/torch/rnn/blob/master/examples/recurrent-visual-attention.lua
  • (license: https://github.com/torch/rnn/blob/master/LICENSE.txt)
• https://github.com/seann999/tensorflow_mnist_ram
  • (MIT license: https://github.com/seann999/tensorflow_mnist_ram/blob/master/LICENSE)
• https://github.com/kevinzakka/recurrent-visual-attention

installation

$ pip install thrillington
(thrillington because there is already a ram on PyPI, and because https://en.wikipedia.org/wiki/Thrillington)

usage

The library can be run from the command line with a config file.

$ ram train ./RAM_config-2018-10-21.ini

...

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config.train.resume is False,
will save new model and optimizer to checkpoint: /home/you/data/ram_output/results_20181021/checkpoints/ckpt

Epoch: 1/200 - learning rate: 0.001000

282.5s - hybrid loss: 1.690 - acc: 6.000: 100%|██████████| 10000/10000 [04:42<00:00, 35.65it/s]
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mean accuracy: 9.97
mean losses: LossTuple(loss_reinforce=-1.1296023, loss_baseline=0.09972435, loss_action=2.3005059, loss_hybrid=1.2706277)

Epoch: 2/200 - learning rate: 0.001000

282.8s - hybrid loss: 1.223 - acc: 10.000: 100%|██████████| 10000/10000 [04:42<00:00, 35.50it/s]
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...

For a detailed explanation of the config file format, please see here

CHANGELOG

To see past changes and work in progress, please check out the CHANGELOG.