tfsummaryvis/README.md

# tfsummaryvis

> Visualise textual Tensorflow summaries as a directed graph.

Tensorflow summaries are invaluable to understand how a model is put together. They usually look like this:

```
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 bidirectional (Bidirectiona  (None, 100, 256)         336896    
 l)                                                              
                                                                 
 bidirectional_1 (Bidirectio  (None, 256)              394240    
 nal)                                                            
                                                                 
 dense (Dense)               (None, 2)                 514       
                                                                 
=================================================================
Total params: 731,650
Trainable params: 731,650
Non-trainable params: 0
_________________________________________________________________
```

Pretty easy to understand, right? But what about [this monster](./samples/deeplabv3%2B.txt)? Or [this one](mono-failure.txt) (this one doesn't actually work, do not implement at home)?

Larger models (and those aren't just a simple stack of layers) can be a nightmare to understand - even with a summary - hence this project was born :-)

It converts the above textual tensorflow summary into a directed graph with [nomnoml](https://nomnoml.com).

## Demo
Try the demo here: <https://starbeamrainbowlabs.com/labs/tfsummaryvis/>


## Getting started
If you want to run this project locally, first start by cloning the repository:

```bash
git clone https://git.starbeamrainbowlabs.com/sbrl/tfsummaryvis.git
cd tfsummaryvis;
```

Then, install dependencies:

```bash
npm install
```

Next, build the client-side app:

```bash
npm run build
```

Finally, serve with your [favourite static web server](https://gist.github.com/willurd/5720255):

```bash
npx serve dist/
```


## Contributing
Contributions are very welcome - both issues and pull requests! Please mention in your pull request that you release your work under the MPL-2.0 (see below).

If you're feeling that way inclined, the sponsor button at the top of the page (if you're on GitHub) will take you to my [Liberapay profile](https://liberapay.com/sbrl) if you'd like to donate to say an extra thank you :-)


## License
tfsummaryvis is released under the Mozilla Public License 2.0. The full license text is included in the `LICENSE` file in this repository. Tldr legal have a [great summary](https://tldrlegal.com/license/mozilla-public-license-2.0-(mpl-2)) of the license if you're interested.
Initial commit 2023-02-16 20:17:05 +00:00			`# tfsummaryvis`

fill in readme 2023-02-16 20:34:47 +00:00			`> Visualise textual Tensorflow summaries as a directed graph.`

			`Tensorflow summaries are invaluable to understand how a model is put together. They usually look like this:`

			```
			`Model: "sequential"`
			`_________________________________________________________________`
			`Layer (type) Output Shape Param #`
			`=================================================================`
			`bidirectional (Bidirectiona (None, 100, 256) 336896`
			`l)`

			`bidirectional_1 (Bidirectio (None, 256) 394240`
			`nal)`

			`dense (Dense) (None, 2) 514`

			`=================================================================`
			`Total params: 731,650`
			`Trainable params: 731,650`
			`Non-trainable params: 0`
			`_________________________________________________________________`
			```

			`Pretty easy to understand, right? But what about [this monster](./samples/deeplabv3%2B.txt)? Or [this one](mono-failure.txt) (this one doesn't actually work, do not implement at home)?`

			`Larger models (and those aren't just a simple stack of layers) can be a nightmare to understand - even with a summary - hence this project was born :-)`

			`It converts the above textual tensorflow summary into a directed graph with [nomnoml](https://nomnoml.com).`

			`## Demo`
			`Try the demo here: <https://starbeamrainbowlabs.com/labs/tfsummaryvis/>`


			`## Getting started`
			`If you want to run this project locally, first start by cloning the repository:`

			```bash
			`git clone https://git.starbeamrainbowlabs.com/sbrl/tfsummaryvis.git`
			`cd tfsummaryvis;`
			```

			`Then, install dependencies:`

			```bash
			`npm install`
			```

			`Next, build the client-side app:`

			```bash
			`npm run build`
			```

			`Finally, serve with your [favourite static web server](https://gist.github.com/willurd/5720255):`

			```bash
			`npx serve dist/`
			```


			`## Contributing`
			`Contributions are very welcome - both issues and pull requests! Please mention in your pull request that you release your work under the MPL-2.0 (see below).`

			`If you're feeling that way inclined, the sponsor button at the top of the page (if you're on GitHub) will take you to my [Liberapay profile](https://liberapay.com/sbrl) if you'd like to donate to say an extra thank you :-)`


			`## License`
			tfsummaryvis is released under the Mozilla Public License 2.0. The full license text is included in the `LICENSE` file in this repository. Tldr legal have a [great summary](https://tldrlegal.com/license/mozilla-public-license-2.0-(mpl-2)) of the license if you're interested.