semvec/pypi-packages huggingface dataset:
thrillington 0.0.2a1
Replication of "Recurrent models of visual attention", Mnih et al. 2014
License: BSD
Requires Python: >=3.6.0


# 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

...

  0%|          | 0/10000 [00:00<?, ?it/s]

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]
  0%|          | 0/10000 [00:00<?, ?it/s]

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]
  0%|          | 0/10000 [00:00<?, ?it/s]
...
```

For a detailed explanation of the config file format, please see [here](./doc/config.md)

## CHANGELOG
To see past changes and work in progress, please check out the [CHANGELOG](./doc/CHANGELOG.md).
dsin100daysv12 6.0.0.dev0
A web-based notebook environment for interactive computing
License: BSD
Requires Python: >=3.4
Keywords: Interactive,Interpreter,Shell,Web

The Jupyter Notebook is a web application that allows you to create and
share documents that contain live code, equations, visualizations, and
explanatory text. The Notebook has support for multiple programming
languages, sharing, and interactive widgets.

Read `the documentation <https://jupyter-notebook.readthedocs.io>`_
for more information.