https://github.com/amasky/ram

Recurrent Models of Visual Attention (RAM) with Chainer
https://github.com/amasky/ram

Last synced: 11 days ago
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Recurrent Models of Visual Attention (RAM) with Chainer

• Host: GitHub
• URL: https://github.com/amasky/ram
• Owner: amasky
• License: mit
• Created: 2016-05-26T05:51:02.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-04-18T11:25:10.000Z (over 7 years ago)
• Last Synced: 2024-05-21T18:08:38.937Z (6 months ago)
• Language: Python
• Homepage:
• Size: 6.32 MB
• Stars: 44
• Watchers: 3
• Forks: 8
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Recurrent Attention Model

Recurrent Attention Model with Chainer based on the following paper  

[arXiv:1406.6247](http://arxiv.org/abs/1406.6247): Recurrent Models of Visual Attention [Volodymyr Mnih+ 2014]  

## Features  

* RAM model on Chainer  

* script to train RAM and classify digits with RAM 

* including Original MNIST, Translated MNIST, and Cluttered Translated MNIST tasks  

* 1.13% error on 28x28 MNIST (RAM, 6 glimpses, 8x8, 1 scale)  

* 1.58% error on 60x60 Translated MNIST (RAM, 6 glimpses, 12x12, 3 scales)  

* 5.30% error on 60x60 Cluttered Translated MNIST (RAM, 6 glimpses, 12x12, 3 scales)  

## Examples  

glimpses and output probabilities at each time step  

* Original MNIST  

![example on original MNIST](figures/ram_original.png)  

* Translated MNIST  

![example on translated MNIST](figures/ram_translated.png)  

* Cluttered Translated MNIST  

![example on cluttered translated MNIST](figures/ram_cluttered.png)  

## Dependencies  

Python(2 or 3), Chainer, PIL, matplotlib, tqdm  

## Usage  

train.py: optimizes weights of a RAM model and ouputs learned weights to \*.chainermodel file every 100 epoch

* with "--original" for 28x28 original MNIST task, "--translated" for 60x60 translated MNIST, and "--cluttered" for 60x60 cluttered translated MNIST  

* training for Cluttered Translated MNIST will take around half a day with CPU

* train on CPU with default setting or GPU with "-g YOUR_GPU_DEVICE_ID"

```shellsession

➜ python train.py --original  

```

infer.py: plot result of inference by a trained RAM model (result will show up with your matplotlib's backend)  

```shellsession

➜ python infer.py --original -m models/ram_original_epoch800.chainermodel  

```