This directory contains a Jupyter notebook that demonstrates how to generate trippy images using a number of Convolutional Neural Network image generation techniques implemented with TensorFlow.
- visualizing individual feature channels and their combinations to explore the space of patterns learned by the neural network (see GoogLeNet and VGG16 galleries)
- embedding TensorBoard graph visualizations into Jupyter notebooks
- producing high-resolution images with tiled computation (example)
- using Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost
- generating DeepDream-like images with TensorFlow
You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes embedded graph visualizations. You can still see them online using nbviewer service.
Make sure you have all the dependencies installed.
To open the notebook, run
ipython notebook command in this directory, and
select 'Deep Dream with Inception Network.ipynb' in the opened browser window.