https://github.com/gaborvecsei/swiss-army-tensorboard

A tool which helps you to release the true potential of Tensorboard
https://github.com/gaborvecsei/swiss-army-tensorboard

keras log logger python tensorboard tensorflow visualization

Last synced: about 1 month ago
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A tool which helps you to release the true potential of Tensorboard

• Host: GitHub
• URL: https://github.com/gaborvecsei/swiss-army-tensorboard
• Owner: gaborvecsei
• Created: 2018-10-14T19:14:50.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-08-11T08:50:57.000Z (over 5 years ago)
• Last Synced: 2024-11-07T21:45:44.631Z (3 months ago)
• Topics: keras, log, logger, python, tensorboard, tensorflow, visualization
• Language: Python
• Homepage: https://gaborvecsei.com
• Size: 76.2 KB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Swiss Army Tensorboard

A tool which helps you to release the true potential of [Tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard).



## Loggers

You can keep almost everything under a single Tensorboard, which can be really useful for presentations, tutorials,

experiment results.

- Text logger

- Continuous text logger

- Scalar logger

- Histogram logger

- Image logger

## Setup

`pip install git+https://github.com/gaborvecsei/Swiss-Army-Tensorboard.git`

(Or you can clone the repo and `python setup.py install`)

## Example

There is an example for every logger inside [`logger_examples.py`](example/logger_examples.py).

Just a quick snippet how easy to use this package:

```python

import numpy as np

from swiss_army_tensorboard import tfboard_loggers

scalar_logger = tfboard_loggers.TFBoardScalarLogger("./log_folder")

for i, t in enumerate(np.arange(0.0, 1.0, 0.01)):

    val = np.sin(2 * np.pi * t)

    scalar_logger.log_scalar("scalar_tag", val, i)

```

Text logger output (inside Tensorboard):



## Reference

*Image* and *Histogram loggers* are made based on the following gist:

*[1]* https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514

Keras train-validation callbacks is based on this stackoverflow answer:

*[2]* https://stackoverflow.com/a/48393723/5108062

## About

Gábor Vecsei

- [Website](https://gaborvecsei.com)

- [Personal Blog](https://gaborvecsei.wordpress.com/)

- [LinkedIn](https://www.linkedin.com/in/gaborvecsei)

- [Twitter](https://twitter.com/GAwesomeBE)

- [Github](https://github.com/gaborvecsei)