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Small fixes to RNN-RBM tutorial #17

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Feb 21, 2013
Merged

Small fixes to RNN-RBM tutorial #17

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3 changes: 2 additions & 1 deletion code/rnnrbm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -271,9 +271,10 @@ def test_rnnrbm(batch_size=100, num_epochs=200):
model = RnnRbm()
model.train(glob.glob('../data/Nottingham/train/*.mid'),
batch_size=batch_size, num_epochs=num_epochs)
return model

if __name__ == '__main__':
test_rnnrbm()
model = test_rnnrbm()
model.generate('sample1.mid')
model.generate('sample2.mid')
pylab.show()
9 changes: 6 additions & 3 deletions doc/rnnrbm.txt
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Expand Up @@ -11,7 +11,7 @@ Modeling and generating sequences of polyphonic music with the RNN-RBM
and `restricted Boltzmann machines (RBM) <rbm.html>`_.

.. note::
The code for this section is available for download here: `rnnrbm.py <../code/rnnrbm.py>`_.
The code for this section is available for download here: `rnnrbm.py <code/rnnrbm.py>`_.

You will need the modified `Python MIDI package (GPL license) <http://www.iro.umontreal.ca/~lisa/deep/midi.zip>`_ in your ``$PYTHONPATH`` or in the working directory in order to convert MIDI files to and from piano-rolls.
The script also assumes that the content of the `Nottingham Database of folk tunes <http://www.iro.umontreal.ca/~lisa/deep/data/Nottingham.zip>`_ has been extracted in the ``../data`` directory.
Expand Down Expand Up @@ -266,7 +266,7 @@ We now have all the necessary ingredients to start training our network on real
n_hidden_recurrent)

gradient = T.grad(cost, params, consider_constant=[v_sample])
updates_train.update(dict((p, p - lr * g) for p, g in zip(params,
updates_train.update(((p, p - lr * g) for p, g in zip(params,
gradient)))
self.train_function = theano.function([v], monitor,
updates=updates_train)
Expand All @@ -288,7 +288,10 @@ We now have all the necessary ingredients to start training our network on real

assert len(files) > 0, 'Training set is empty!' \
' (did you download the data files?)'
dataset = [midiread(f, self.r, self.dt).piano_roll for f in files]
dataset = [midiread(f, self.r,
self.dt).piano_roll.astype(theano.config.floatX)
for f in files]

try:
for epoch in xrange(num_epochs):
numpy.random.shuffle(dataset)
Expand Down