def main():
# First, let's create a simple feedforward MLP with one hidden layer as a Prototype.
mlp = Prototype()
mlp.add(
BasicLayer(input_size=28 * 28,
output_size=1000,
activation='rectifier',
noise='dropout'))
mlp.add(SoftmaxLayer(output_size=10))
# Now, we get to choose what values we want to monitor, and what datasets we would like to monitor on!
# Each Model (in our case, the Prototype), has a get_monitors method that will return a useful
# dictionary of {string_name: monitor_theano_expression} for various computations of the model we might
# care about. By default, this method returns an empty dictionary - it was the model creator's job to
# include potential monitor values.
mlp_monitors = mlp.get_monitors()
mlp_channel = MonitorsChannel(name="error")
for name, expression in mlp_monitors.items():
mlp_channel.add(
Monitor(name=name,
expression=expression,
train=True,
valid=True,
test=True))
# create some monitors for statistics about the hidden and output weights!
# let's look at the mean, variance, and standard deviation of the weights matrices.
weights_channel = MonitorsChannel(name="weights")
hiddens_1 = mlp[0].get_params()[0]
hiddens1_mean = T.mean(hiddens_1)
weights_channel.add(
Monitor(name="hiddens_mean", expression=hiddens1_mean, train=True))
hiddens_2 = mlp[1].get_params()[0]
hiddens2_mean = T.mean(hiddens_2)
weights_channel.add(
Monitor(name="out_mean", expression=hiddens2_mean, train=True))
# create our plot object to do live plotting!
plot = Plot(bokeh_doc_name="Monitor Tutorial",
monitor_channels=[mlp_channel, weights_channel],
open_browser=True)
# use SGD optimizer
optimizer = SGD(model=mlp,
dataset=MNIST(concat_train_valid=False),
n_epoch=500,
save_frequency=100,
batch_size=600,
learning_rate=.01,
lr_decay=False,
momentum=.9,
nesterov_momentum=True)
# train, with the plot!
optimizer.train(plot=plot)
def main():
# First, let's create a simple feedforward MLP with one hidden layer as a Prototype.
mlp = Prototype()
mlp.add(BasicLayer(input_size=28*28, output_size=1000, activation='rectifier', noise='dropout'))
mlp.add(SoftmaxLayer(output_size=10))
# Now, we get to choose what values we want to monitor, and what datasets we would like to monitor on!
# Each Model (in our case, the Prototype), has a get_monitors method that will return a useful
# dictionary of {string_name: monitor_theano_expression} for various computations of the model we might
# care about. By default, this method returns an empty dictionary - it was the model creator's job to
# include potential monitor values.
mlp_monitors = mlp.get_monitors()
mlp_channel = MonitorsChannel(name="error")
for name, expression in mlp_monitors.items():
mlp_channel.add(Monitor(name=name, expression=expression, train=True, valid=True, test=True))
# create some monitors for statistics about the hidden and output weights!
# let's look at the mean, variance, and standard deviation of the weights matrices.
weights_channel = MonitorsChannel(name="weights")
hiddens_1 = mlp[0].get_params()[0]
hiddens1_mean = T.mean(hiddens_1)
weights_channel.add(Monitor(name="hiddens_mean", expression=hiddens1_mean, train=True))
hiddens_2 = mlp[1].get_params()[0]
hiddens2_mean = T.mean(hiddens_2)
weights_channel.add(Monitor(name="out_mean", expression=hiddens2_mean, train=True))
# create our plot object to do live plotting!
plot = Plot(bokeh_doc_name="Monitor Tutorial", monitor_channels=[mlp_channel, weights_channel], open_browser=True)
# use SGD optimizer
optimizer = SGD(model=mlp,
dataset=MNIST(concat_train_valid=False),
n_epoch=500,
save_frequency=100,
batch_size=600,
learning_rate=.01,
lr_decay=False,
momentum=.9,
nesterov_momentum=True)
# train, with the plot!
optimizer.train(plot=plot)
def setup_optimization(model, n_epoch, mnist_dataset):
# setup optimizer stochastic gradient descent
optimizer = SGD(model=model,
dataset=mnist_dataset,
n_epoch=n_epoch,
batch_size=600,
learning_rate=.01,
momentum=.9,
nesterov_momentum=True,
save_frequency=500,
early_stop_threshold=0.997)
# create a Monitor to view progress on a metric other than training cost
error = Monitor('error', model.get_monitors()['softmax_error'], train=True, valid=True, test=True)
return optimizer, error
def main():
########################################
# Initialization things with arguments #
########################################
# use these arguments to get results from paper referenced above
_train_args = {"epochs": 1000, # maximum number of times to run through the dataset
"batch_size": 100, # number of examples to process in parallel (minibatch)
"min_batch_size": 1, # the minimum number of examples for a batch to be considered
"save_freq": 1, # how many epochs between saving parameters
"stop_threshold": .9995, # multiplier for how much the train cost to improve to not stop early
"stop_patience": 500, # how many epochs to wait to see if the threshold has been reached
"learning_rate": .25, # initial learning rate for SGD
"lr_decay": 'exponential', # the decay function to use for the learning rate parameter
"lr_decay_factor": .995, # by how much to decay the learning rate each epoch
"momentum": 0.5, # the parameter momentum amount
'momentum_decay': False, # how to decay the momentum each epoch (if applicable)
'momentum_factor': 0, # by how much to decay the momentum (in this case not at all)
'nesterov_momentum': False, # whether to use nesterov momentum update (accelerated momentum)
}
config_root_logger()
log.info("Creating a new GSN")
mnist = MNIST(concat_train_valid=True)
gsn = GSN(layers=2,
walkbacks=4,
hidden_size=1500,
visible_activation='sigmoid',
hidden_activation='tanh',
input_size=28*28,
tied_weights=True,
hidden_add_noise_sigma=2,
input_salt_and_pepper=0.4,
outdir='outputs/test_gsn/',
vis_init=False,
noiseless_h1=True,
input_sampling=True,
weights_init='uniform',
weights_interval='montreal',
bias_init=0,
cost_function='binary_crossentropy')
recon_cost_channel = MonitorsChannel(name='cost')
recon_cost_channel.add(Monitor('recon_cost', gsn.get_monitors()['recon_cost'], test=True))
recon_cost_channel.add(Monitor('noisy_recon_cost', gsn.get_monitors()['noisy_recon_cost'], test=True))
# Load initial weights and biases from file
# params_to_load = '../../../outputs/gsn/mnist/trained_epoch_395.pkl'
# gsn.load_params(params_to_load)
optimizer = SGD(model=gsn, dataset=mnist, **_train_args)
# optimizer = AdaDelta(model=gsn, dataset=mnist, epochs=200, batch_size=100, learning_rate=1e-6)
optimizer.train(monitor_channels=recon_cost_channel)
# Save some reconstruction output images
n_examples = 100
xs_test = mnist.test_inputs[:n_examples]
noisy_xs_test = gsn.f_noise(xs_test)
reconstructed = gsn.run(noisy_xs_test)
# Concatenate stuff
stacked = numpy.vstack(
[numpy.vstack([xs_test[i * 10: (i + 1) * 10],
noisy_xs_test[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]])
for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (gsn.image_height, gsn.image_width), (10, 30))
)
number_reconstruction.save(gsn.outdir + 'reconstruction.png')
log.info("saved output image!")
# Construct image from the weight matrix
image = PIL.Image.fromarray(
tile_raster_images(
X=gsn.weights_list[0].get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=closest_to_square_factors(gsn.hidden_size),
tile_spacing=(1, 1)
)
)
image.save(gsn.outdir + "gsn_mnist_weights.png")
def add_list_layers():
# You can also add lists of layers at a time (or as initialization) to a Prototype! This lets you specify
# more complex interactions between layers!
hidden1 = BasicLayer(input_size=28 * 28,
output_size=512,
activation='rectifier',
noise='dropout')
hidden2 = BasicLayer(inputs_hook=(512, hidden1.get_outputs()),
output_size=512,
activation='rectifier',
noise='dropout')
class_layer = SoftmaxLayer(inputs_hook=(512, hidden2.get_outputs()),
output_size=10)
mlp = Prototype([hidden1, hidden2, class_layer])
return mlp
if __name__ == '__main__':
mlp = sequential_add_layers()
optimizer = SGD(model=mlp,
dataset=MNIST(concat_train_valid=True),
n_epoch=500,
batch_size=600,
learning_rate=.01,
momentum=.9,
nesterov_momentum=True)
optimizer.train()
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
# although this is recommended over print statements everywhere
import logging
import opendeep.log.logger as logger
logger.config_root_logger()
log = logging.getLogger(__name__)
log.info("Creating RBM!")
# grab the MNIST dataset
mnist = MNIST(concat_train_valid=False)
# create the RBM
rng = numpy.random.RandomState(1234)
mrg = theano.tensor.shared_randomstreams.RandomStreams(rng.randint(2**30))
rbm = RBM(input_size=28*28, hidden_size=500, k=15, weights_init='uniform', weights_interval=4*numpy.sqrt(6./(28*28+500)), rng=rng)
# rbm.load_params('rbm_trained.pkl')
# make an optimizer to train it (AdaDelta is a good default)
optimizer = SGD(model=rbm, dataset=mnist, n_epoch=15, batch_size=20, learning_rate=0.1, lr_decay=False, nesterov_momentum=False)
# optimizer = AdaDelta(model=rbm, dataset=mnist, n_epoch=200, batch_size=100, learning_rate=1e-6)
# perform training!
optimizer.train()
# test it on some images!
test_data = mnist.getSubset(TEST)[0]
test_data = test_data[:25].eval()
# use the run function!
preds = rbm.run(test_data)
# Construct image from the test matrix
image = Image.fromarray(
tile_raster_images(
X=test_data,
img_shape=(28, 28),
tile_shape=(5, 5),
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 main():
########################################
# Initialization things with arguments #
########################################
# use these arguments to get results from paper referenced above
_train_args = {"n_epoch": 1000, # maximum number of times to run through the dataset
"batch_size": 100, # number of examples to process in parallel (minibatch)
"minimum_batch_size": 1, # the minimum number of examples for a batch to be considered
"save_frequency": 1, # how many epochs between saving parameters
"early_stop_threshold": .9995, # multiplier for how much the train cost to improve to not stop early
"early_stop_length": 500, # how many epochs to wait to see if the threshold has been reached
"learning_rate": .25, # initial learning rate for SGD
"lr_decay": 'exponential', # the decay function to use for the learning rate parameter
"lr_factor": .995, # by how much to decay the learning rate each epoch
"momentum": 0.5, # the parameter momentum amount
'momentum_decay': False, # how to decay the momentum each epoch (if applicable)
'momentum_factor': 0, # by how much to decay the momentum (in this case not at all)
'nesterov_momentum': False, # whether to use nesterov momentum update (accelerated momentum)
}
config_root_logger()
log.info("Creating a new GSN")
mnist = MNIST(concat_train_valid=True)
gsn = GSN(layers=2,
walkbacks=4,
hidden_size=1500,
visible_activation='sigmoid',
hidden_activation='tanh',
input_size=28*28,
tied_weights=True,
hidden_add_noise_sigma=2,
input_salt_and_pepper=0.4,
outdir='outputs/test_gsn/',
vis_init=False,
noiseless_h1=True,
input_sampling=True,
weights_init='uniform',
weights_interval='montreal',
bias_init=0,
cost_function='binary_crossentropy')
recon_cost_channel = MonitorsChannel(name='cost')
recon_cost_channel.add(Monitor('recon_cost', gsn.get_monitors()['recon_cost'], test=True))
recon_cost_channel.add(Monitor('noisy_recon_cost', gsn.get_monitors()['noisy_recon_cost'], test=True))
# Load initial weights and biases from file
# params_to_load = '../../../outputs/gsn/mnist/trained_epoch_395.pkl'
# gsn.load_params(params_to_load)
optimizer = SGD(model=gsn, dataset=mnist, **_train_args)
# optimizer = AdaDelta(model=gsn, dataset=mnist, n_epoch=200, batch_size=100, learning_rate=1e-6)
optimizer.train(monitor_channels=recon_cost_channel)
# Save some reconstruction output images
import opendeep.data.dataset as datasets
n_examples = 100
xs_test, _ = mnist.getSubset(datasets.TEST)
xs_test = xs_test[:n_examples].eval()
noisy_xs_test = gsn.f_noise(xs_test)
reconstructed = gsn.run(noisy_xs_test)
# Concatenate stuff
stacked = numpy.vstack(
[numpy.vstack([xs_test[i * 10: (i + 1) * 10],
noisy_xs_test[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]])
for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (gsn.image_height, gsn.image_width), (10, 30))
)
number_reconstruction.save(gsn.outdir + 'reconstruction.png')
log.info("saved output image!")
# Construct image from the weight matrix
image = PIL.Image.fromarray(
tile_raster_images(
X=gsn.weights_list[0].get_value(borrow=True).T,
img_shape=(28, 28),
tile_shape=closest_to_square_factors(gsn.hidden_size),
tile_spacing=(1, 1)
)
)
image.save(gsn.outdir + "gsn_mnist_weights.png")