https://github.com/hardmaru/write-rnn-tensorflow
Generative Handwriting using LSTM Mixture Density Network with TensorFlow
https://github.com/hardmaru/write-rnn-tensorflow
Last synced: 1 day ago
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Generative Handwriting using LSTM Mixture Density Network with TensorFlow
- Host: GitHub
- URL: https://github.com/hardmaru/write-rnn-tensorflow
- Owner: hardmaru
- Created: 2015-12-12T08:12:18.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2018-02-10T08:17:23.000Z (about 7 years ago)
- Last Synced: 2025-03-24T06:45:46.978Z (21 days ago)
- Language: Python
- Size: 52.7 MB
- Stars: 701
- Watchers: 29
- Forks: 164
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
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README
## Generative Handwriting Demo using TensorFlow
An attempt to implement the random handwriting generation portion of Alex Graves' [paper](http://arxiv.org/abs/1308.0850).
See my blog post at [blog.otoro.net](http://blog.otoro.net/2015/12/12/handwriting-generation-demo-in-tensorflow) for more information.
### How to use
I tested the implementation on TensorFlow r0.11 and Pyton 3. I also used the following libraries to help:
```
svgwrite
IPython.display.SVG
IPython.display.display
xml.etree.ElementTree
argparse
pickle
```
### Training
You will need permission from [these wonderful people](http://www.iam.unibe.ch/fki/databases/iam-on-line-handwriting-database) people to get the IAM On-Line Handwriting data. Unzip `lineStrokes-all.tar.gz` into the data subdirectory, so that you end up with `data/lineStrokes/a01`, `data/lineStrokes/a02`, etc. Afterwards, running `python train.py` will start the training process.
A number of flags can be set for training if you wish to experiment with the parameters. The default values are in `train.py`
```
--rnn_size RNN_SIZE size of RNN hidden state
--num_layers NUM_LAYERS number of layers in the RNN
--model MODEL rnn, gru, or lstm
--batch_size BATCH_SIZE minibatch size
--seq_length SEQ_LENGTH RNN sequence length
--num_epochs NUM_EPOCHS number of epochs
--save_every SAVE_EVERY save frequency
--grad_clip GRAD_CLIP clip gradients at this value
--learning_rate LEARNING_RATE learning rate
--decay_rate DECAY_RATE decay rate for rmsprop
--num_mixture NUM_MIXTURE number of gaussian mixtures
--data_scale DATA_SCALE factor to scale raw data down by
--keep_prob KEEP_PROB dropout keep probability
```
### Generating a Handwriting Sample
I've included a pretrained model in `/save` so it should work out of the box. Running `python sample.py --filename example_name --sample_length 1000` will generate 4 .svg files for each example, with 1000 points.
### IPython interactive session.
If you wish to experiment with this code interactively, just run `%run -i sample.py` in an IPython console, and then the following code is an example on how to generate samples and show them inside IPython.
```
[strokes, params] = model.sample(sess, 800)
draw_strokes(strokes, factor=8, svg_filename = 'sample.normal.svg')
draw_strokes_random_color(strokes, factor=8, svg_filename = 'sample.color.svg')
draw_strokes_random_color(strokes, factor=8, per_stroke_mode = False, svg_filename = 'sample.multi_color.svg')
draw_strokes_eos_weighted(strokes, params, factor=8, svg_filename = 'sample.eos.svg')
draw_strokes_pdf(strokes, params, factor=8, svg_filename = 'sample.pdf.svg')
```
Have fun-
## License
MIT