deep_dream_challenge

KO | EN

gitlite — search

#python #hacktoberfest #react #ai #go #android #machine-learning #rust #java #cli #kubernetes #vue #awesome-list #security

deep_dream_challenge

(★ 86)

Deep Dream Challenge code by @SIrajology on Youtube (Learn Python for Data Science #5)

파일 탐색기

최종 버전 다운로드 (.zip)

deep_dream.py
README.md

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# deep_dream_challenge
Deep Dream Challenge code by @SIrajology on [Youtube](https://youtu.be/MrBzgvUNr4w)

##Overview

This is the code for the Deep Dream challenge for 'Learn Python for Data Science #5' by @Sirajology on [YouTube](https://youtu.be/MrBzgvUNr4wY). The code uses the Tensorflow Machine Learning library to generate a trippy image
from a given input image.

##Dependencies

* numpy (http://www.numpy.org/)
* functools
* PIL (http://stackoverflow.com/questions/20060096/installing-pil-with-pip)
* tensorflow (https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#pip-installation)
* matplotlib (http://matplotlib.org/1.5.1/users/installing.html)
* urllib (https://pypi.python.org/pypi/urllib3)
* os
* zipfile

Install missing dependencies using [pip](https://pip.pypa.io/en/stable/installing/)

##Demo Usage

Once you have your dependencies installed via pip, run the demo script in terminal via

```
python deep_dream.py
```

##Challenge

The instructions are

1. Add a function to the existing deep dream python file that converts not just an image, but a video clip to deep dream.

*HINT* Think of a video as a collection of images (frames).

If you want to use your own template, that's fine too. Submit your code in the comments section and I'll announce the winner in
the next video. Good luck!

##Credits

Thanks Google and [Liu](https://github.com/LiuzcEECS) I've reformatted this code to make it easier to grok.

deep_dream_challenge

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

deep_dream_challenge

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README