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@HendrikStrobelt
HendrikStrobelt committed Jun 3, 2016
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1 change: 1 addition & 0 deletions .gitignore
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*.pyc
3 changes: 3 additions & 0 deletions .virtualrc
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source /etc/profile
source ~/.bash_profile
source ~/Coding/python_venv/LSTM/bin/activate
93 changes: 93 additions & 0 deletions README.md
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# LSTMVis
Visualization Toolbox for Long Short Term Memory networks (LSTMs)

![Alt text](client/assets/example1.png)


## Getting started

Install python requirements in your virtual environment:

```
pip install -r requirements.txt
```

Install bower requirements:

```
cd client; bower install; cd ..
```

start server:

```
python server.py -dir <datadir>
```

open browser at [http://localhost:8888](http://localhost:8888)


## Data Directory
LSTMVis parses all subdirectories of `<datadir>` for config files `lstm.yml`.
A typical `<datadir>` might look like this:

```
<datadir>
├── parens <--- minimal example
│   ├── lstm.yml
│   ├── states.h5
│   ├── train.h5
│   └── words.dict
├── ptb_word <-- with search functionality and meta data (part-of-speech,..)
│   ├── embeddings.h5
│   ├── indexdir
│   │   └── ...
│   ├── lstm.yml
│   ├── pos.dict
│   ├── pos.h5
│   ├── states.h5
│   ├── train.h5
│   ├── weight.h5
│   └── words.dict
```



a simple example of `lstm.yml` is:

```yaml
name: rls - ep 10 # project name

files: # assign files to reference name
states: rls_200_ep10_states.h5 # HDF5 files have to end with .h5 !!!
train: rls.h5
words: rls.dict # dict files have to end in .dict !!

# OPTIONAL: If you have information about word embedding
# word_embedding:
# file: embed
# path: weights

word_sequence: # defines the word sequence
file: train # HDF5 file
path: words # path to table within HDF5
dict_file: words # dictionary to map IDs from HDF5 to words

states:
file: states # HDF5 files containing the state for each position
types: [
{type: state, layer: 1, path: states1}, # type={state, output}, layer=[1..x], path = HDF5 path
{type: output, layer: 1, path: output1},
{type: state, layer: 2, path: states2},
{type: output, layer: 2, path: output2}
]

```

## Generate Data

TODO ...

## Credits

LSTMVis is a project of Hendrik Strobelt, Alexander 'Sasha' Rush, Sebastian Gehrmann, and Bernd Huber at Harvard SEAS.
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104 changes: 104 additions & 0 deletions client/assets/schema_interactive.svg
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28 changes: 28 additions & 0 deletions client/bower.json
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{
"name": "LSTMVisClient",
"version": "0.1.0",
"authors": [
"LSTM Team"
],
"description": "",
"main": "",
"moduleType": [],
"license": "GPL",
"homepage": "",
"private": true,
"ignore": [
"**/.*",
"node_modules",
"bower_components",
"test",
"tests"
],
"dependencies": {
"jquery": "~2.2",
"d3": "~3.5",
"lodash": "~4.6",
"bootswatch": "v3.3.6+1",
"font-awesome": "fontawesome#~4.5.0",
"lodash.math": "*"
}
}
147 changes: 147 additions & 0 deletions client/index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>LSTM Vis</title>
<script src="bower_components/jquery/dist/jquery.min.js"></script>
<script src="bower_components/d3/d3.min.js"></script>
<script src="bower_components/lodash/dist/lodash.min.js"></script>
<script src="bower_components/bootstrap/dist/js/bootstrap.min.js"></script>

<link rel="stylesheet" type="text/css" href="bower_components/bootswatch/cosmo/bootstrap.min.css">
<link rel="stylesheet" type="text/css" href="bower_components/font-awesome/css/font-awesome.min.css">

</head>
<body>
<div class="container">
<!--<div class="row">-->
<h1 class="text-center "><img src="assets/logo.png" height="40"> - Visualization for LSTMs</h1>
<!--</div>-->
<!--<h2>Datasets:</h2>-->
<!--<ul class="list-group" id="ds_list">-->
<!--</ul>-->
<div id="ds_list"></div>


</div>

<script>
var url = '';
var query = url + '/api/info/';
$.ajax(query, {
dataType: 'json',
success: function (info) {
console.log(info);

d3.text('assets/schema_interactive.svg', function (e_, schema_svg) {

// console.log(schema_svg);


var lis = d3.select('#ds_list').selectAll('div').data(info)
.enter().append('div')
.attr({
class: "row"
});

lis.append('h3').text(function (d, i) {return d.info.name});
lis.append('hr');

function add_info(parent, title, accesor) {
parent.append('div').attr({
class: 'col-md-2 col-xs-3 text-right'
}).style({
'font-weight': 'bold'
}).html(title);

parent.append('div').attr({
class: 'col-md-10 col-xs-9 text-left'
}).html(function (d) {return accesor(d)});

}

add_info(lis, 'file', function (d, i) {return d.project});
add_info(lis, 'meta', function (d, i) {
var keys = Object.keys(d.info.meta);
return (keys.length > 0) ? keys.join(', ') : '---'
});
add_info(lis, 'embedding_size', function (d, i) {return d.info.word_embedding.size.join(' x ')});
add_info(lis, 'sequence_length', function (d, i) {return d.info.word_sequence.size[0]});


lis.each(function (ds, ds_id) {
var that = d3.select(this);
ds.info.states.types.forEach(function (d, i) {

add_info(that, d.file + ':' + d.path, function () {
return '<a href="pattern_finder.html?data_set=' + ds_id + '&source=' + d.file + '::' + d.path + '">'
+ 'layer ' + d.layer + ' ' + d.type
+ ' ( size: ' + d.size.join(' x ') + ')'
+ (d.unsigned ? ' - <b>unsigned</b>' : '')
+ '</a>'
})
});

// var no_layers = d3.max(ds.info.states.types.map(function (d, i) {return d.layer})) + 1;
// var all_data_subsets = ds.info.states.types.map(function (d, i) {return d.layer + d.type});
// var all_subset_paths = ds.info.states.types.map(function (d, i) {return d.path});
//
// var svgs = that.append('p').attr({class: 'text-center'});
//
// _.range(1, no_layers).forEach(function (layer_x) {
//
// var span = svgs.append('p').html(schema_svg);
// span.select('.picto_states').attr({
// hidden: function () {
// return _.includes(all_data_subsets, layer_x + 'state') ? null : true;
// }
// }).on('click', function (d) {
// var i = _.indexOf(all_data_subsets, layer_x + 'state');
// window.open('pattern_finder.html?data_set='+ds_id+'&source='+all_subset_paths[i])
// console.log(ds_id, layer_x, all_subset_paths[i]);
// });
// span.select('.picto_output').attr({
// hidden: function () {
// return _.includes(all_data_subsets, layer_x + 'output') ? null : true;
// }
// }).on('click', function (d) {
// var i = _.indexOf(all_data_subsets, layer_x + 'output');
// window.open('pattern_finder.html?data_set='+ds_id+'&source='+all_subset_paths[i])
//
//
// console.log(ds_id, layer_x, all_subset_paths[i]);
// });
//
// })


});

})


//<div class="panel panel-success">
// <div class="panel-heading">
// <h3 class="panel-title">Panel success</h3>
// </div>
// <div class="panel-body">
// Panel content
// </div>
//</div>

// info.forEach(function(ds){
//
//
//
// })


}
})


</script>


</body>
</html>

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