Adding an autoencoder tensorflow example #119
Open
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
ghost
reviewed
Jul 10, 2019
|
|
||
| 8. **yolo-tensorflow** | ||
|
|
||
| YOLO (You Only Look Once) is a state-of-the-are, real-time object detection system. This example trained a yolo model with pascal-voc 2007 data. |
Contributor
Author
There was a problem hiding this comment.
I made a pull request of 312 lines where ONE of these 312 is clearly a typographical error and, since I didn't disable "Allow edits from maintainers.", is easily fixed by anyone who reviews this pull request. If your only objective here is making a joke, I should say you are at the wrong place.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This pull request adds an autoencoder example, using tensorflow.
Using by default MNIST, it encodes the images into a compressed layer, that then can be decoded into the original image.
Here are some example images (the images on the left are the originals, and the one on the right after being encoded and decoded):
Thanks!