Generative Handwriting using LSTM Mixture Density Network with TensorFlow

svgwrite
IPython.display.SVG
IPython.display.display
xml.etree.ElementTree
argparse
pickle

--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

[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')