Metadata-Version: 2.1
Name: thrillington
Version: 0.0.2a1
Summary: Replication of "Recurrent models of visual attention", Mnih et al. 2014
Home-page: https://github.com/NickleDave/thrillington
Author: Percy "Thrills" Thrillington
Author-email: nicholdav@gmail.com
License: BSD
Description: 
        # 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).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
