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,
n_epoch=200,
batch_size=100,
minimum_batch_size=2,
learning_rate=.001,
save_frequency=10,
early_stop_length=200,
momentum=False,
momentum_decay=False,
nesterov_momentum=False)
optimizer = AdaDelta(model=rnnrbm,
dataset=midi,
n_epoch=200,
batch_size=100,
minimum_batch_size=2,
# learning_rate=1e-4,
learning_rate=1e-6,
save_frequency=10,
early_stop_length=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
def run_sequence(sequence=0):
log.info("Creating RNN-RBM for sequence %d!" % sequence)
# grab the MNIST dataset
mnist = MNIST(sequence_number=sequence, concat_train_valid=True)
outdir = "outputs/rnnrbm/mnist_%d/" % sequence
# create the RNN-RBM
rng = numpy.random.RandomState(1234)
mrg = RandomStreams(rng.randint(2 ** 30))
rnnrbm = RNN_RBM(input_size=28 * 28,
hidden_size=1000,
rnn_hidden_size=100,
k=15,
weights_init='uniform',
weights_interval=4 * numpy.sqrt(6. / (28 * 28 + 500)),
rnn_weights_init='identity',
rnn_hidden_activation='relu',
rnn_weights_std=1e-4,
mrg=mrg,
outdir=outdir)
# load pretrained rbm on mnist
# rnnrbm.load_params(outdir + 'trained_epoch_200.pkl')
# make an optimizer to train it (AdaDelta is a good default)
optimizer = AdaDelta(model=rnnrbm,
dataset=mnist,
n_epoch=200,
batch_size=100,
minimum_batch_size=2,
learning_rate=1e-8,
save_frequency=10,
early_stop_length=200)
crossentropy = Monitor('crossentropy', rnnrbm.get_monitors()['crossentropy'], test=True)
error = Monitor('error', rnnrbm.get_monitors()['mse'], test=True)
plot = Plot(bokeh_doc_name='rnnrbm_mnist_%d' % sequence, monitor_channels=[crossentropy, error], open_browser=True)
# perform training!
optimizer.train(plot=plot)
# use the generate function!
log.debug("generating images...")
generated, ut = rnnrbm.generate(initial=None, n_steps=400)
# Construct image
image = Image.fromarray(
tile_raster_images(
X=generated,
img_shape=(28, 28),
tile_shape=(20, 20),
tile_spacing=(1, 1)
)
)
image.save(outdir + "rnnrbm_mnist_generated.png")
log.debug('saved generated.png')
# Construct image from the weight matrix
image = Image.fromarray(
tile_raster_images(
X=rnnrbm.W.get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=closest_to_square_factors(rnnrbm.hidden_size),
tile_spacing=(1, 1)
)
)
image.save(outdir + "rnnrbm_mnist_weights.png")
log.debug("done!")
del mnist
del rnnrbm
del optimizer
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
def run_sequence(sequence=0):
log.info("Creating RNN-RBM for sequence %d!" % sequence)
# grab the MNIST dataset
mnist = MNIST(sequence_number=sequence, concat_train_valid=True)
outdir = "outputs/rnnrbm/mnist_%d/" % sequence
# create the RNN-RBM
rng = numpy.random.RandomState(1234)
mrg = RandomStreams(rng.randint(2**30))
rnnrbm = RNN_RBM(input_size=28 * 28,
hidden_size=1000,
rnn_hidden_size=100,
k=15,
weights_init='uniform',
weights_interval=4 * numpy.sqrt(6. / (28 * 28 + 500)),
rnn_weights_init='identity',
rnn_hidden_activation='relu',
rnn_weights_std=1e-4,
mrg=mrg,
outdir=outdir)
# load pretrained rbm on mnist
# rnnrbm.load_params(outdir + 'trained_epoch_200.pkl')
# make an optimizer to train it (AdaDelta is a good default)
optimizer = AdaDelta(model=rnnrbm,
dataset=mnist,
n_epoch=200,
batch_size=100,
minimum_batch_size=2,
learning_rate=1e-8,
save_frequency=10,
early_stop_length=200)
crossentropy = Monitor('crossentropy',
rnnrbm.get_monitors()['crossentropy'],
test=True)
error = Monitor('error', rnnrbm.get_monitors()['mse'], test=True)
plot = Plot(bokeh_doc_name='rnnrbm_mnist_%d' % sequence,
monitor_channels=[crossentropy, error],
open_browser=True)
# perform training!
optimizer.train(plot=plot)
# use the generate function!
log.debug("generating images...")
generated, ut = rnnrbm.generate(initial=None, n_steps=400)
# Construct image
image = Image.fromarray(
tile_raster_images(X=generated,
img_shape=(28, 28),
tile_shape=(20, 20),
tile_spacing=(1, 1)))
image.save(outdir + "rnnrbm_mnist_generated.png")
log.debug('saved generated.png')
# Construct image from the weight matrix
image = Image.fromarray(
tile_raster_images(X=rnnrbm.W.get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=closest_to_square_factors(
rnnrbm.hidden_size),
tile_spacing=(1, 1)))
image.save(outdir + "rnnrbm_mnist_weights.png")
log.debug("done!")
del mnist
del rnnrbm
del optimizer
