class TestMuse(unittest.TestCase):
def setUp(self):
print("setting up!")
# get the muse dataset
self.muse = MuseData()
# get the jsb dataset
# self.jsb = JSBChorales()
# get nottingham dataset
self.nottingham = Nottingham()
# get the piano-midi-de dataset
# self.piano = PianoMidiDe()
def testSizes(self):
print('muse train %s' % str(self.muse.train.shape.eval()[0]))
assert self.muse.train.shape.eval()[0] == numpy.sum([l[0] for l in self.muse.getDataShape(TRAIN)])
assert self.muse.valid.shape.eval()[0] == numpy.sum([l[0] for l in self.muse.getDataShape(VALID)])
assert self.muse.test.shape.eval()[0] == numpy.sum([l[0] for l in self.muse.getDataShape(TEST)])
print('nottingham train %s' % str(self.nottingham.train.shape.eval()[0]))
assert self.nottingham.train.shape.eval()[0] == numpy.sum([l[0] for l in self.nottingham.getDataShape(TRAIN)])
assert self.nottingham.valid.shape.eval()[0] == numpy.sum([l[0] for l in self.nottingham.getDataShape(VALID)])
assert self.nottingham.test.shape.eval()[0] == numpy.sum([l[0] for l in self.nottingham.getDataShape(TEST)])
def tearDown(self):
del self.muse
# del self.jsb
del self.nottingham
# del self.piano
print("done!")
def setUp(self):
print("setting up!")
# get the muse dataset
self.muse = MuseData()
# get the jsb dataset
# self.jsb = JSBChorales()
# get nottingham dataset
self.nottingham = Nottingham()
def setUp(self):
print("setting up!")
# get the muse dataset
self.muse = MuseData()
# get the jsb dataset
# self.jsb = JSBChorales()
# get nottingham dataset
self.nottingham = Nottingham()
def setUp(self):
print("setting up!")
config_root_logger()
# get the muse dataset
self.muse = MuseData(path='../../../datasets/MuseData')
# get the jsb dataset
self.jsb = JSBChorales(path='../../../datasets/JSB Chorales')
# get nottingham dataset
self.nottingham = Nottingham(path='../../../datasets/Nottingham')
# get the piano-midi-de dataset
self.piano = PianoMidiDe(path='../../../datasets/Piano-midi.de')
def setUp(self):
print "setting up!"
# configure the root logger
logger.config_root_logger()
# get a logger for this session
self.log = logging.getLogger(__name__)
# get the muse dataset
self.muse = MuseData()
# get the jsb dataset
# self.jsb = JSBChorales()
# get nottingham dataset
self.nottingham = Nottingham()
def setUp(self):
# configure the root logger
logger.config_root_logger()
# get a logger for this session
self.log = logging.getLogger(__name__)
# get the muse dataset
self.muse = MuseData()
# get the jsb dataset
self.jsb = JSBChorales()
# get nottingham dataset
self.nottingham = Nottingham()
# get the piano-midi-de dataset
self.piano = PianoMidiDe()
class TestMuse(unittest.TestCase):
def setUp(self):
print("setting up!")
# get the muse dataset
self.muse = MuseData()
# get the jsb dataset
# self.jsb = JSBChorales()
# get nottingham dataset
self.nottingham = Nottingham()
# get the piano-midi-de dataset
# self.piano = PianoMidiDe()
def testSizes(self):
print('muse train %s' % str(self.muse.train.shape.eval()[0]))
assert self.muse.train.shape.eval()[0] == numpy.sum(
[l[0] for l in self.muse.getDataShape(TRAIN)])
assert self.muse.valid.shape.eval()[0] == numpy.sum(
[l[0] for l in self.muse.getDataShape(VALID)])
assert self.muse.test.shape.eval()[0] == numpy.sum(
[l[0] for l in self.muse.getDataShape(TEST)])
print('nottingham train %s' %
str(self.nottingham.train.shape.eval()[0]))
assert self.nottingham.train.shape.eval()[0] == numpy.sum(
[l[0] for l in self.nottingham.getDataShape(TRAIN)])
assert self.nottingham.valid.shape.eval()[0] == numpy.sum(
[l[0] for l in self.nottingham.getDataShape(VALID)])
assert self.nottingham.test.shape.eval()[0] == numpy.sum(
[l[0] for l in self.nottingham.getDataShape(TEST)])
def tearDown(self):
del self.muse
# del self.jsb
del self.nottingham
# del self.piano
print("done!")
class TestMuse(unittest.TestCase):
def setUp(self):
print "setting up!"
# configure the root logger
logger.config_root_logger()
# get a logger for this session
self.log = logging.getLogger(__name__)
# get the muse dataset
self.muse = MuseData()
# get the jsb dataset
# self.jsb = JSBChorales()
# get nottingham dataset
self.nottingham = Nottingham()
# get the piano-midi-de dataset
# self.piano = PianoMidiDe()
def testSizes(self):
print 'muse train', self.muse.train.shape.eval()[0]
assert self.muse.train.shape.eval()[0] == numpy.sum([l[0] for l in self.muse.getDataShape(TRAIN)])
assert self.muse.valid.shape.eval()[0] == numpy.sum([l[0] for l in self.muse.getDataShape(VALID)])
assert self.muse.test.shape.eval()[0] == numpy.sum([l[0] for l in self.muse.getDataShape(TEST)])
print 'nottingham train', self.nottingham.train.shape.eval()[0]
assert self.nottingham.train.shape.eval()[0] == numpy.sum([l[0] for l in self.nottingham.getDataShape(TRAIN)])
assert self.nottingham.valid.shape.eval()[0] == numpy.sum([l[0] for l in self.nottingham.getDataShape(VALID)])
assert self.nottingham.test.shape.eval()[0] == numpy.sum([l[0] for l in self.nottingham.getDataShape(TEST)])
def tearDown(self):
del self.muse
# del self.jsb
del self.nottingham
# del self.piano
print "done!"
def run_midi(dataset):
log.info("Creating RNN-RBM for dataset %s!", dataset)
outdir = "outputs/rnnrbm/%s/" % dataset
# grab the MIDI dataset
if dataset == 'nottingham':
midi = Nottingham()
elif dataset == 'jsb':
midi = JSBChorales()
elif dataset == 'muse':
midi = MuseData()
elif dataset == 'piano_de':
midi = PianoMidiDe()
else:
raise AssertionError("dataset %s not recognized." % dataset)
# create the RNN-RBM
# rng = numpy.random
# rng.seed(0xbeef)
# mrg = RandomStreams(seed=rng.randint(1 << 30))
rng = numpy.random.RandomState(1234)
mrg = RandomStreams(rng.randint(2**30))
# rnnrbm = RNN_RBM(input_size=88,
# hidden_size=150,
# rnn_hidden_size=100,
# k=15,
# weights_init='gaussian',
# weights_std=0.01,
# rnn_weights_init='gaussian',
# rnn_weights_std=0.0001,
# rng=rng,
# outdir=outdir)
rnnrbm = RNN_RBM(
input_size=88,
hidden_size=150,
rnn_hidden_size=100,
k=15,
weights_init='gaussian',
weights_std=0.01,
rnn_weights_init='identity',
rnn_hidden_activation='relu',
# rnn_weights_init='gaussian',
# rnn_hidden_activation='tanh',
rnn_weights_std=0.0001,
mrg=mrg,
outdir=outdir)
# make an optimizer to train it
optimizer = SGD(model=rnnrbm,
dataset=midi,
epochs=200,
batch_size=100,
min_batch_size=2,
learning_rate=.001,
save_freq=10,
stop_patience=200,
momentum=False,
momentum_decay=False,
nesterov_momentum=False)
optimizer = AdaDelta(
model=rnnrbm,
dataset=midi,
epochs=200,
batch_size=100,
min_batch_size=2,
# learning_rate=1e-4,
learning_rate=1e-6,
save_freq=10,
stop_patience=200)
ll = Monitor('pseudo-log', rnnrbm.get_monitors()['pseudo-log'], test=True)
mse = Monitor('frame-error',
rnnrbm.get_monitors()['mse'],
valid=True,
test=True)
plot = Plot(bokeh_doc_name='rnnrbm_midi_%s' % dataset,
monitor_channels=[ll, mse],
open_browser=True)
# perform training!
optimizer.train(plot=plot)
# use the generate function!
generated, _ = rnnrbm.generate(initial=None, n_steps=200)
dt = 0.3
r = (21, 109)
midiwrite(outdir + 'rnnrbm_generated_midi.mid', generated, r=r, dt=dt)
if has_pylab:
extent = (0, dt * len(generated)) + r
pylab.figure()
pylab.imshow(generated.T,
origin='lower',
aspect='auto',
interpolation='nearest',
cmap=pylab.cm.gray_r,
extent=extent)
pylab.xlabel('time (s)')
pylab.ylabel('MIDI note number')
pylab.title('generated piano-roll')
# Construct image from the weight matrix
image = Image.fromarray(
tile_raster_images(
X=rnnrbm.W.get_value(borrow=True).T,
img_shape=closest_to_square_factors(rnnrbm.input_size),
tile_shape=closest_to_square_factors(rnnrbm.hidden_size),
tile_spacing=(1, 1)))
image.save(outdir + 'rnnrbm_midi_weights.png')
log.debug("done!")
del midi
del rnnrbm
del optimizer