Generative Handwriting using LSTM Mixture Density Network with TensorFlow
Latest commit fe32dcb Jan 17, 2017 @hardmaru committed on GitHub Merge pull request #13 from sygi/master
tf.inv -> reciprocal

Generative Handwriting Demo using TensorFlow



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

See my blog post at 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:



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 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

--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 --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 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-