Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/hnarayanan/artistic-style-transfer
Convolutional neural networks for artistic style transfer.
https://github.com/hnarayanan/artistic-style-transfer
convolutional-neural-networks keras talk tensorflow tutorial-code
Last synced: 1 day ago
JSON representation
Convolutional neural networks for artistic style transfer.
- Host: GitHub
- URL: https://github.com/hnarayanan/artistic-style-transfer
- Owner: hnarayanan
- Created: 2017-02-12T15:44:44.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-12-07T11:29:42.000Z (about 7 years ago)
- Last Synced: 2025-01-15T11:40:44.483Z (8 days ago)
- Topics: convolutional-neural-networks, keras, talk, tensorflow, tutorial-code
- Language: Jupyter Notebook
- Homepage: https://harishnarayanan.org/writing/artistic-style-transfer/
- Size: 16 MB
- Stars: 355
- Watchers: 17
- Forks: 79
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Convolutional neural networks for artistic style transfer
This repository contains (TensorFlow and Keras) code that goes along
with a [related blog post][blog-post] and [talk
(PDF)][talk-slides]. Together, they act as a systematic look at
convolutional neural networks from theory to practice, using artistic
style transfer as a motivating example. The blog post provides context
and covers the underlying theory, while working through the Jupyter
notebooks in this repository offers a more hands-on learning
experience.
If you have any questions about any of this stuff, feel free to [open
an issue][support-issue] or tweet at me: [@copingbear][twitter].
## Setup
1. Install Python (2.7), pip and virtualenv on your machine. The
instructions to do this depend on your operating system (Linux, macOS,
Windows), but there are many tutorials on the internet that should
help you get started.
2. Once you have the above setup, it is quite easy to setup the
requirements for the notebooks in this repository. First you clone a
copy of this repository:
````
git clone https://github.com/hnarayanan/artistic-style-transfer.git
````
3. Then you navigate to this folder in your shell and then install the
requirements needed for the Jupyter notebooks.
````
cd artistic-style-transfer
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
````
4. If it doesn't exist, create a file called `~/.keras/keras.json` and
make sure it looks like the following:
````
{
"image_dim_ordering": "tf",
"epsilon": 1e-07,
"floatx": "float32",
"backend": "tensorflow"
}
````
5. That's it! You can now start Jupyter and browse, open, run and
modify the notebooks.
````
jupyter notebook
````
## Contents
### iPython Notebooks
1. [A linear classifier for MNIST data][linear-mnist]
2. [A neural network-based classifier for MNIST data (Attempt 1)][neural-mnist-1]
3. [A neural network-based classifier for MNIST data (Attempt 2)][neural-mnist-2]
4. [A convolutional neural network-based classifier for MNIST data][convnet-mnist]
5. [VGG Net (16) on ImageNet, the easy way][vggnet-imagenet]
6. [Artistic style transfer with a repurposed VGG Net (16)][style-transfer]
### External Resources
1. [Related blog post][blog-post]
2. [Related talk slides][talk-slides]
[blog-post]: https://harishnarayanan.org/writing/artistic-style-transfer/
[talk-slides]: https://speakerdeck.com/hnarayanan/convolutional-neural-networks-for-artistic-style-transfer
[support-issue]: https://github.com/hnarayanan/artistic-style-transfer/issues
[twitter]: https://twitter.com/copingbear
[linear-mnist]: notebooks/1_Linear_Image_Classifier.ipynb
[neural-mnist-1]: notebooks/2_Neural_Network-based_Image_Classifier-1.ipynb
[neural-mnist-2]: notebooks/3_Neural_Network-based_Image_Classifier-2.ipynb
[convnet-mnist]: notebooks/4_Convolutional_Neural_Network-based_Image_Classifier.ipynb
[vggnet-imagenet]: notebooks/5_VGG_Net_16_the_easy_way.ipynb
[style-transfer]: notebooks/6_Artistic_style_transfer_with_a_repurposed_VGG_Net_16.ipynb