Generative Handwriting using LSTM Mixture Density Network with TensorFlow

Related tags

Deep Learningwrite-rnn-tensorflow
Overview

Generative Handwriting Demo using TensorFlow

example

example

An attempt to implement the random handwriting generation portion of Alex Graves' paper.

See my blog post at blog.otoro.net 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 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')

example1a example1b example1c example1d example1e

Have fun-

License

MIT

Owner
hardmaru
I make simple things with neural networks.
hardmaru
GitHub Repository
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