Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

update readme #10

Open
wants to merge 2 commits into
base: master
Choose a base branch
from
Open

update readme #10

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
50fb872
update readme
shivam2601 Oct 25, 2018
98eaa7a
update readme
shivam2601 Oct 25, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
15 changes: 1 addition & 14 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,21 +18,8 @@ Generated Sketches (Temperature = 0.1):

# Basic Usage

I tested the implementation on TensorFlow 0.50. I also used the following libraries to help:
I tested the implementation on TensorFlow 0.50. I also used the following libraries to help``

```
svgwrite
IPython.display.SVG
IPython.display.display
xml.etree.ElementTree
argparse
cPickle
svg.path
```

## Loading in Training Data

The training data is located inside the `data` subdirectory. In this repo, I've included `kanji.cpkl` which is a preprocessed array of KanjiVG characters.

To add a new set of training data, for example, from the [TU Berlin Sketch Database](http://cybertron.cg.tu-berlin.de/eitz/projects/classifysketch/), you have to create a subdirectory, say `tuberlin` inside the `data` directory, and in addition create a directory of the same name in the `save` directory. So you end up with `data/tuberlin/` and `save/tuberlin`, where `tuberlin` is defined as a name field for flags in the training and sample programs later on. `save/tuberlin` will contain the check-pointed trained models later on.

Expand Down