def run_experiment(args):
# set environment variable for theano
os.environ['THEANO_FLAGS'] = "device=gpu" + str(args.gpu)
from learning.experiment import Experiment
from learning.training import DSGNHTTrainer
from learning.termination import EarlyStopping
from learning.monitor import MonitorLL, DLogModelParams, SampleFromP, MonitorPosteriorMean
from learning.dataset import Omniglot
from learning.preproc import PermuteColumns, Binarize
from learning.models.dgm_vae import VAEDGMLayerStack
from learning.models.vae import VAE, StandardVAETop
np.set_printoptions(precision=2)
logger.debug("Arguments %s" % args)
tags = []
np.random.seed(23)
# n_samples to evaluate model
n_samples_epoch = [1, 5, 25, 100]
n_samples_final = [1, 5, 10, 25, 100, 500, 1000, 5000]
# no. posterior samples for posterior mean
postsamples = [int(s) for s in args.postsamples.split(",")]
n_X = 28 * 28
p_layers = []
q_layers = []
model = None
# build the variational auto-encoder layer stack
if args.layers == 1:
latent_units = [50]
hidden_units_q = [[200, 200]]
hidden_units_p = [[200, 200]]
n_Y = latent_units[0]
p_layers.append(
VAE(n_X=n_X,
n_Y=n_Y,
det_units=hidden_units_p[0],
data_type="binary",
bias=None))
p_layers.append(StandardVAETop(n_X=n_Y))
q_layers.append(
VAE(n_X=n_Y,
n_Y=n_X,
det_units=hidden_units_q[0],
data_type="continuous",
bias=None))
model = VAEDGMLayerStack(
p_layers=p_layers,
q_layers=q_layers,
)
model.setup()
elif args.layers == 2:
latent_units = [100, 50]
hidden_units_p = [[200, 200], [100, 100]]
hidden_units_q = [[100, 100], [200, 200]]
n_Y = latent_units[0]
p_layers.append(
VAE(n_X=n_X,
n_Y=latent_units[0],
det_units=hidden_units_p[0],
data_type="binary",
bias=None))
q_layers.append(
VAE(n_X=latent_units[0],
n_Y=n_X,
det_units=hidden_units_q[1],
data_type="continuous",
bias=None))
p_layers.append(
VAE(n_X=latent_units[0],
n_Y=latent_units[1],
det_units=hidden_units_p[1],
data_type="continuous",
bias=None))
q_layers.append(
VAE(n_X=latent_units[1],
n_Y=latent_units[0],
det_units=hidden_units_q[0],
data_type="continuous",
bias=None))
p_layers.append(StandardVAETop(n_X=latent_units[1]))
model = VAEDGMLayerStack(
p_layers=p_layers,
q_layers=q_layers,
)
model.setup()
assert model is not None
# parameters
def param_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
if value == 0.0:
return "00"
exp = np.floor(np.log10(value))
leading = ("%e" % value)[0]
return "%s%d" % (leading, -exp)
# Learning rate
lr_p = args.lr_p
tags += ["lp" + param_tag(lr_p)]
lr_q = args.lr_q
tags += ["lq" + param_tag(lr_q)]
# LR decay
if args.lrdecay != 1.0:
tags += ["lrdecay" + param_tag(args.lrdecay - 1.)]
# Samples
n_samples = args.samples
tags += ["spl%d" % n_samples]
# Batch size
batch_size = args.batchsize
tags += ["bs%d" % batch_size]
# n_steps_simu
n_steps_simu = args.n_simu
tags += ["ns%d" % n_steps_simu]
# n_steps_optm
n_steps_optm = args.n_optm
tags += ["no%d" % n_steps_optm]
# momentum_decay
momentum_decay = args.momentum_decay
tags += ["md" + param_tag(momentum_decay)]
# Dataset
if args.shuffle:
np.random.seed(23)
preproc = [PermuteColumns()]
tags += ["shuffle"]
else:
preproc = []
binarize_preproc = preproc + [Binarize(late=True)]
dataset = Omniglot(which_set='train', preproc=binarize_preproc)
valiset = Omniglot(which_set='valid', preproc=binarize_preproc)
testset = Omniglot(which_set='test', preproc=binarize_preproc)
# lookahead
lookahead = args.lookahead
tags += ["lah%d" % lookahead]
tags.sort()
expname = "dsgnht-%s-%slayer" % ("-".join(tags), str(args.layers))
if args.report:
expname = "report/" + expname
logger.info("Running %s" % expname)
#-----------------------------------------------------------------------------
dlog_model_params_monitor = DLogModelParams()
generate_data_monitor = SampleFromP(n_samples=100)
trainer = DSGNHTTrainer(
batch_size=batch_size,
n_samples=n_samples,
n_steps_simu=n_steps_simu,
n_steps_optm=n_steps_optm,
learning_rate_p=lr_p,
learning_rate_q=lr_q,
lr_decay=args.lrdecay,
momentum_decay=momentum_decay,
dataset=dataset,
model=model,
termination=EarlyStopping(lookahead=lookahead,
min_epochs=10,
max_epochs=999999),
epoch_monitors=[
dlog_model_params_monitor,
generate_data_monitor,
MonitorLL(name="valiset", data=valiset, n_samples=n_samples_epoch),
],
final_monitors=[
dlog_model_params_monitor,
generate_data_monitor,
MonitorLL(name="final-testset",
data=testset,
n_samples=n_samples_final,
level=logging.CRITICAL),
],
posterior_mean_samples=postsamples,
posterior_mean_monitor=MonitorPosteriorMean(),
)
experiment = Experiment()
experiment.set_trainer(trainer)
experiment.setup_output_dir(expname)
experiment.setup_logging()
experiment.print_summary()
if args.cont is None:
logger.info("Starting experiment ...")
experiment.run_experiment()
else:
logger.info("Continuing experiment %s ...." % args.cont)
experiment.continue_experiment(args.cont + "/results.h5", row=-1)
logger.info("Finished. Wrinting metadata")
experiment.print_summary()
def run_experiment(args):
# set environment variable for theano
os.environ['THEANO_FLAGS'] = "device=gpu" + str(args.gpu)
from learning.experiment import Experiment
from learning.training import DSGNHTTrainer
from learning.termination import EarlyStopping
from learning.monitor import MonitorLL, DLogModelParams, SampleFromP, MonitorPosteriorMean
from learning.dataset import MNIST
from learning.preproc import PermuteColumns, Binarize
from learning.models.dgm import DGMLayerStack
from learning.models.sbn import SBN, SBNTop
from learning.models.dsbn import DSBN
from learning.models.darn import DARN, DARNTop
from learning.models.nade import NADE, NADETop
np.set_printoptions(precision=2)
logger.debug("Arguments %s" % args)
tags = []
np.random.seed(23)
# Layer models
layer_models = {
"sbn": (SBN, SBNTop),
"dsbn": (DSBN, SBNTop),
"darn": (DARN, DARNTop),
"nade": (NADE, NADETop),
}
assert args.p_model in layer_models
assert args.q_model in layer_models
p_layer, p_top = layer_models[args.p_model]
q_layer, q_top = layer_models[args.q_model]
# n_samples to evaluate model
n_samples_epoch = [1, 5, 25, 100]
n_samples_final = [1, 5, 10, 25, 100, 500, 1000, 10000, 100000]
if (args.p_model in ['darn', 'nade']) or (args.q_model in ['darn', 'nade'
]):
n_samples_epoch = [1, 5, 25]
n_samples_final = [1, 5, 10, 25, 100, 500]
# no. posterior samples for posterior mean
postsamples = [int(s) for s in args.postsamples.split(",")]
# Layer sizes
layer_sizes = [int(s) for s in args.layer_sizes.split(",")]
n_X = 28 * 28
p_layers = []
q_layers = []
for ls in layer_sizes:
n_Y = ls
p_layers.append(p_layer(n_X=n_X, n_Y=n_Y))
q_layers.append(q_layer(n_X=n_Y, n_Y=n_X))
n_X = n_Y
p_layers.append(p_top(n_X=n_X))
model = DGMLayerStack(p_layers=p_layers, q_layers=q_layers)
model.setup()
# parameters
def param_tag(value):
""" Convert a float into a short tag-usable string representation. E.g.:
0.1 -> 11
0.01 -> 12
0.001 -> 13
0.005 -> 53
"""
if value == 0.0:
return "00"
exp = np.floor(np.log10(value))
leading = ("%e" % value)[0]
return "%s%d" % (leading, -exp)
# Learning rate
lr_p = args.lr_p
tags += ["lp" + param_tag(lr_p)]
lr_q = args.lr_q
tags += ["lq" + param_tag(lr_q)]
# LR decay
if args.lrdecay != 1.0:
tags += ["lrdecay" + param_tag(args.lrdecay - 1.)]
# Samples
n_samples = args.samples
tags += ["spl%d" % n_samples]
# Batch size
batch_size = args.batchsize
tags += ["bs%d" % batch_size]
# n_steps_simu
n_steps_simu = args.n_simu
tags += ["ns%d" % n_steps_simu]
# n_steps_optm
n_steps_optm = args.n_optm
tags += ["no%d" % n_steps_optm]
# momentum_decay
momentum_decay = args.momentum_decay
tags += ["md" + param_tag(momentum_decay)]
# Dataset
if args.shuffle:
np.random.seed(23)
preproc = [PermuteColumns()]
tags += ["shuffle"]
else:
preproc = []
if args.rebinarize:
binarize_preproc = preproc + [Binarize(late=True)]
dataset = MNIST(which_set='train',
preproc=binarize_preproc,
n_datapoints=50000)
valiset = MNIST(which_set='valid',
preproc=binarize_preproc,
n_datapoints=10000)
testset = MNIST(which_set='test',
preproc=binarize_preproc,
n_datapoints=10000)
# testset = MNIST(fname="mnist_salakhutdinov.pkl.gz", which_set='test', preproc=preproc, n_datapoints=10000)
tags += ["rb"]
else:
dataset = MNIST(fname="mnist_salakhutdinov.pkl.gz",
which_set='train',
preproc=preproc,
n_datapoints=50000)
valiset = MNIST(fname="mnist_salakhutdinov.pkl.gz",
which_set='valid',
preproc=preproc,
n_datapoints=10000)
testset = MNIST(fname="mnist_salakhutdinov.pkl.gz",
which_set='test',
preproc=preproc,
n_datapoints=10000)
# lookahead
lookahead = args.lookahead
tags += ["lah%d" % lookahead]
tags.sort()
expname = "dsgnht-%s-%s-%s-%s" % ("-".join(tags), args.p_model,
args.q_model, "-".join(
[str(s) for s in layer_sizes]))
if args.report:
expname = "report/" + expname
logger.info("Running %s" % expname)
#-----------------------------------------------------------------------------
dlog_model_params_monitor = DLogModelParams()
generate_data_monitor = SampleFromP(n_samples=100)
trainer = DSGNHTTrainer(
batch_size=batch_size,
n_samples=n_samples,
n_steps_simu=n_steps_simu,
n_steps_optm=n_steps_optm,
learning_rate_p=lr_p,
learning_rate_q=lr_q,
lr_decay=args.lrdecay,
momentum_decay=momentum_decay,
dataset=dataset,
model=model,
termination=EarlyStopping(lookahead=lookahead,
min_epochs=10,
max_epochs=999999),
epoch_monitors=[
dlog_model_params_monitor,
generate_data_monitor,
MonitorLL(name="valiset", data=valiset, n_samples=n_samples_epoch),
],
final_monitors=[
dlog_model_params_monitor,
generate_data_monitor,
MonitorLL(name="final-testset",
data=testset,
n_samples=n_samples_final,
level=logging.CRITICAL),
],
posterior_mean_samples=postsamples,
posterior_mean_monitor=MonitorPosteriorMean(),
)
experiment = Experiment()
experiment.set_trainer(trainer)
experiment.setup_output_dir(expname)
experiment.setup_logging()
experiment.print_summary()
if args.cont is None:
logger.info("Starting experiment ...")
experiment.run_experiment()
else:
logger.info("Continuing experiment %s ...." % args.cont)
experiment.continue_experiment(args.cont + "/results.h5", row=-1)
logger.info("Finished. Wrinting metadata")
experiment.print_summary()
p_layers=p_layers,
q_layers=q_layers,
)
trainer = Trainer(
n_samples=10,
learning_rate_p=1e-6,
#learning_rate_q=1e-6,
learning_rate_q=0.0,
#learning_rate_s=1e-6,
learning_rate_s=1e-6,
weight_decay=0.0,
batch_size=25,
dataset=trainset,
model=model,
termination=EarlyStopping(lookahead=10),
epoch_monitors=[
DLogModelParams(),
#SampleFromP(n_samples=100),
MonitorLL(name="valiset", data=valiset, n_samples=[1, 5, 10, 25, 100]),
],
final_monitors=[
MonitorLL(name="final-valiset",
data=valiset,
n_samples=[1, 5, 10, 25, 100, 500, 1000]),
MonitorLL(name="final-testset",
data=testset,
n_samples=[1, 5, 10, 25, 100, 500, 1000]),
],
#step_monitors=[
# DLogModelParams(),
model = LayerStack(
p_layers=p_layers,
q_layers=q_layers,
)
trainer = Trainer(
n_samples=5,
learning_rate_p=3e-2,
learning_rate_q=3e-2,
learning_rate_s=3e-2,
layer_discount=1.00,
batch_size=25,
dataset=dataset,
model=model,
termination=EarlyStopping(),
#monitor_nth_step=100,
#step_monitors=[
# MonitorLL(name="valiset", data=valiset, n_samples=[1, 5, 25, 100])
#],
epoch_monitors=[
DLogModelParams(),
MonitorLL(name="valiset", data=valiset, n_samples=[1, 5, 25, 100]),
MonitorLL(name="testset", data=testset, n_samples=[1, 5, 25, 100]),
],
final_monitors=[
MonitorLL(name="final-valiset",
data=valiset,
n_samples=[1, 5, 25, 100]),
MonitorLL(name="final-testset",
data=testset,
)
]
model = LayerStack(
p_layers=p_layers,
q_layers=q_layers,
)
trainer = Trainer(
n_samples=5,
learning_rate_p=1e-3,
learning_rate_q=1e-3,
learning_rate_s=1e-3,
layer_discount=1.0,
batch_size=25,
dataset=dataset,
model=model,
termination=EarlyStopping(min_epochs=250, max_epochs=250),
#step_monitors=[MonitorLL(data=smallset, n_samples=[1, 5, 25, 100])],
epoch_monitors=[
DLogModelParams(),
SampleFromP(n_samples=100)
MonitorLL(name="valiset", data=valiset, n_samples=[100]),
],
final_monitors=[
MonitorLL(name="final-valiset", data=valiset, n_samples=[1, 5, 10, 25, 100, 500, 1000]),
MonitorLL(name="final-testset", data=testset, n_samples=[1, 5, 10, 25, 100, 500, 1000]),
],
#monitor_nth_step=100,
)
