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
- https://github.com/seann999/tensorflow_mnist_ram
- https://github.com/kevinzakka/recurrent-visual-attention
$ pip install thrillington
(thrillington because there is already a ram on PyPI,
and because https://en.wikipedia.org/wiki/Thrillington)
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
To see past changes and work in progress, please check out the CHANGELOG.
- Research funded by the Lifelong Learning Machines program, DARPA/Microsystems Technology Office, DARPA cooperative agreement HR0011-18-2-0019