def train(args):
numpy.random.seed(args.random_seed)
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
print("Invalid logging level requested:", args.log_level)
sys.exit(1)
log_format = "%(asctime)s %(funcName)s: %(message)s"
if args.log_file == "-":
logging.basicConfig(stream=sys.stdout, format=log_format, level=log_level)
else:
logging.basicConfig(filename=log_file, format=log_format, level=log_level)
if args.debug:
theano.config.compute_test_value = "warn"
print("Enabled computing test values for tensor variables.")
print("Warning: GpuArray backend will fail random number generation!")
else:
theano.config.compute_test_value = "off"
theano.config.profile = args.profile
theano.config.profile_memory = args.profile
with h5py.File(args.model_path, "a", driver="core") as state:
if state.keys():
print("Reading vocabulary from existing network state.")
sys.stdout.flush()
vocabulary = Vocabulary.from_state(state)
elif args.vocabulary is None:
print("Constructing vocabulary from training set.")
sys.stdout.flush()
vocabulary = Vocabulary.from_corpus(args.training_set, args.num_classes)
for training_file in args.training_set:
training_file.seek(0)
vocabulary.get_state(state)
else:
print("Reading vocabulary from {}.".format(args.vocabulary))
sys.stdout.flush()
with open(args.vocabulary, "rt", encoding="utf-8") as vocab_file:
vocabulary = Vocabulary.from_file(vocab_file, args.vocabulary_format)
if args.vocabulary_format == "classes":
print("Computing class membership probabilities from " "unigram word counts.")
sys.stdout.flush()
vocabulary.compute_probs(args.training_set)
vocabulary.get_state(state)
print("Number of words in vocabulary:", vocabulary.num_words())
print("Number of word classes:", vocabulary.num_classes())
if args.num_noise_samples > vocabulary.num_classes():
print(
"Number of noise samples ({}) is larger than the number of "
"classes. This doesn't make sense and would cause sampling "
"to fail.".format(args.num_noise_samples)
)
sys.exit(1)
if args.unk_penalty is None:
ignore_unk = False
unk_penalty = None
elif args.unk_penalty == 0:
ignore_unk = True
unk_penalty = None
else:
ignore_unk = False
unk_penalty = args.unk_penalty
num_training_files = len(args.training_set)
if len(args.weights) > num_training_files:
print("You specified more weights than training files.")
sys.exit(1)
weights = numpy.ones(num_training_files).astype(theano.config.floatX)
for index, weight in enumerate(args.weights):
weights[index] = weight
training_options = {
"batch_size": args.batch_size,
"sequence_length": args.sequence_length,
"validation_frequency": args.validation_frequency,
"patience": args.patience,
"stopping_criterion": args.stopping_criterion,
"max_epochs": args.max_epochs,
"min_epochs": args.min_epochs,
"max_annealing_count": args.max_annealing_count,
}
logging.debug("TRAINING OPTIONS")
for option_name, option_value in training_options.items():
logging.debug("%s: %s", option_name, str(option_value))
optimization_options = {
"method": args.optimization_method,
"epsilon": args.numerical_stability_term,
"gradient_decay_rate": args.gradient_decay_rate,
"sqr_gradient_decay_rate": args.sqr_gradient_decay_rate,
"learning_rate": args.learning_rate,
"weights": weights,
"momentum": args.momentum,
"max_gradient_norm": args.gradient_normalization,
"cost_function": args.cost,
"num_noise_samples": args.num_noise_samples,
"noise_sharing": args.noise_sharing,
"ignore_unk": ignore_unk,
"unk_penalty": unk_penalty,
}
logging.debug("OPTIMIZATION OPTIONS")
for option_name, option_value in optimization_options.items():
if type(option_value) is list:
value_str = ", ".join(str(x) for x in option_value)
logging.debug("%s: [%s]", option_name, value_str)
else:
logging.debug("%s: %s", option_name, str(option_value))
if len(args.sampling) > len(args.training_set):
print("You specified more sampling coefficients than training " "files.")
sys.exit(1)
print("Creating trainer.")
sys.stdout.flush()
trainer = Trainer(training_options, vocabulary, args.training_set, args.sampling)
trainer.set_logging(args.log_interval)
print("Building neural network.")
sys.stdout.flush()
if args.architecture == "lstm300" or args.architecture == "lstm1500":
architecture = Architecture.from_package(args.architecture)
else:
with open(args.architecture, "rt", encoding="utf-8") as arch_file:
architecture = Architecture.from_description(arch_file)
network = Network(
architecture,
vocabulary,
trainer.class_prior_probs,
args.noise_dampening,
default_device=args.default_device,
profile=args.profile,
)
print("Compiling optimization function.")
sys.stdout.flush()
optimizer = create_optimizer(optimization_options, network, device=args.default_device, profile=args.profile)
if args.print_graph:
print("Cost function computation graph:")
theano.printing.debugprint(optimizer.gradient_update_function)
trainer.initialize(network, state, optimizer)
if not args.validation_file is None:
print("Building text scorer for cross-validation.")
sys.stdout.flush()
scorer = TextScorer(network, ignore_unk, unk_penalty, args.profile)
print("Validation text:", args.validation_file.name)
validation_mmap = mmap.mmap(args.validation_file.fileno(), 0, prot=mmap.PROT_READ)
validation_iter = LinearBatchIterator(
validation_mmap, vocabulary, batch_size=args.batch_size, max_sequence_length=None
)
trainer.set_validation(validation_iter, scorer)
else:
print("Cross-validation will not be performed.")
validation_iter = None
print("Training neural network.")
sys.stdout.flush()
trainer.train()
if not "layers" in state.keys():
print(
"The model has not been trained. No cross-validations were "
"performed or training did not improve the model."
)
elif not validation_iter is None:
network.set_state(state)
perplexity = scorer.compute_perplexity(validation_iter)
print("Best validation set perplexity:", perplexity)
def train(args):
"""A function that performs the "theanolm train" command.
:type args: argparse.Namespace
:param args: a collection of command line arguments
"""
numpy.random.seed(args.random_seed)
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
print("Invalid logging level requested:", args.log_level)
sys.exit(1)
log_format = '%(asctime)s %(funcName)s: %(message)s'
if args.log_file == '-':
logging.basicConfig(stream=sys.stdout,
format=log_format,
level=log_level)
else:
logging.basicConfig(filename=log_file,
format=log_format,
level=log_level)
if args.debug:
theano.config.compute_test_value = 'warn'
print("Enabled computing test values for tensor variables.")
print("Warning: GpuArray backend will fail random number generation!")
else:
theano.config.compute_test_value = 'off'
theano.config.profile = args.profile
theano.config.profile_memory = args.profile
with h5py.File(args.model_path, 'a', driver='core') as state:
vocabulary = _read_vocabulary(args, state)
if args.num_noise_samples > vocabulary.num_classes():
print("Number of noise samples ({}) is larger than the number of "
"classes. This doesn't make sense and would cause sampling "
"to fail.".format(args.num_noise_samples))
sys.exit(1)
num_training_files = len(args.training_set)
if len(args.weights) > num_training_files:
print("You specified more weights than training files.")
sys.exit(1)
weights = numpy.ones(num_training_files).astype(theano.config.floatX)
for index, weight in enumerate(args.weights):
weights[index] = weight
training_options = {
'batch_size': args.batch_size,
'sequence_length': args.sequence_length,
'validation_frequency': args.validation_frequency,
'patience': args.patience,
'stopping_criterion': args.stopping_criterion,
'max_epochs': args.max_epochs,
'min_epochs': args.min_epochs,
'max_annealing_count': args.max_annealing_count
}
logging.debug("TRAINING OPTIONS")
for option_name, option_value in training_options.items():
logging.debug("%s: %s", option_name, str(option_value))
optimization_options = {
'method': args.optimization_method,
'epsilon': args.numerical_stability_term,
'gradient_decay_rate': args.gradient_decay_rate,
'sqr_gradient_decay_rate': args.sqr_gradient_decay_rate,
'learning_rate': args.learning_rate,
'weights': weights,
'momentum': args.momentum,
'max_gradient_norm': args.gradient_normalization,
'cost_function': args.cost,
'num_noise_samples': args.num_noise_samples,
'noise_sharing': args.noise_sharing,
'exclude_unk': args.exclude_unk
}
logging.debug("OPTIMIZATION OPTIONS")
for option_name, option_value in optimization_options.items():
if isinstance(option_value, list):
value_str = ', '.join(str(x) for x in option_value)
logging.debug("%s: [%s]", option_name, value_str)
else:
logging.debug("%s: %s", option_name, str(option_value))
if len(args.sampling) > len(args.training_set):
print("You specified more sampling coefficients than training "
"files.")
sys.exit(1)
print("Creating trainer.")
sys.stdout.flush()
trainer = Trainer(training_options, vocabulary, args.training_set,
args.sampling)
trainer.set_logging(args.log_interval)
print("Building neural network.")
sys.stdout.flush()
if args.architecture == 'lstm300' or args.architecture == 'lstm1500':
architecture = Architecture.from_package(args.architecture)
else:
with open(args.architecture, 'rt', encoding='utf-8') as arch_file:
architecture = Architecture.from_description(arch_file)
network = Network(architecture,
vocabulary,
trainer.class_prior_probs,
args.noise_dampening,
default_device=args.default_device,
profile=args.profile)
print("Compiling optimization function.")
sys.stdout.flush()
optimizer = create_optimizer(optimization_options,
network,
profile=args.profile)
if args.print_graph:
print("Cost function computation graph:")
theano.printing.debugprint(optimizer.gradient_update_function)
trainer.initialize(network, state, optimizer)
# XXX Write the model instantly back to disk. Just adds word unigram
# counts. This is a temporary hack. Remove at some point.
trainer.get_state(state)
state.flush()
# XXX
if args.validation_file is not None:
print("Building text scorer for cross-validation.")
sys.stdout.flush()
scorer = TextScorer(network,
use_shortlist=True,
exclude_unk=args.exclude_unk,
profile=args.profile)
print("Validation text:", args.validation_file.name)
validation_mmap = mmap.mmap(args.validation_file.fileno(),
0,
prot=mmap.PROT_READ)
validation_iter = \
LinearBatchIterator(validation_mmap,
vocabulary,
batch_size=args.batch_size,
max_sequence_length=args.sequence_length,
map_oos_to_unk=False)
trainer.set_validation(validation_iter, scorer)
else:
print("Cross-validation will not be performed.")
validation_iter = None
print("Training neural network.")
sys.stdout.flush()
trainer.train()
if 'layers' not in state.keys():
print("The model has not been trained. No cross-validations were "
"performed or training did not improve the model.")
elif validation_iter is not None:
network.set_state(state)
perplexity = scorer.compute_perplexity(validation_iter)
print("Best validation set perplexity:", perplexity)
def train(args):
"""A function that performs the "theanolm train" command.
:type args: argparse.Namespace
:param args: a collection of command line arguments
"""
numpy.random.seed(args.random_seed)
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
print("Invalid logging level requested:", args.log_level)
sys.exit(1)
log_format = '%(asctime)s %(funcName)s: %(message)s'
if args.log_file == '-':
logging.basicConfig(stream=sys.stdout,
format=log_format,
level=log_level)
else:
logging.basicConfig(filename=log_file,
format=log_format,
level=log_level)
if args.debug:
theano.config.compute_test_value = 'warn'
logging.info("Enabled computing test values for tensor variables.")
logging.warning("GpuArray backend will fail random number generation!")
else:
theano.config.compute_test_value = 'off'
theano.config.profile = args.profile
theano.config.profile_memory = args.profile
with h5py.File(args.model_path, 'a', driver='core') as state:
vocabulary = _read_vocabulary(args, state)
if args.num_noise_samples > vocabulary.num_classes():
print("Number of noise samples ({}) is larger than the number of "
"classes. This doesn't make sense and would cause unigram "
"sampling to fail.".format(args.num_noise_samples))
sys.exit(1)
num_training_files = len(args.training_set)
if len(args.weights) > num_training_files:
print("You specified more weights than training files.")
sys.exit(1)
weights = numpy.ones(num_training_files).astype(theano.config.floatX)
for index, weight in enumerate(args.weights):
weights[index] = weight
if len(args.sampling) > num_training_files:
print("You specified more sampling coefficients than training "
"files.")
sys.exit(1)
training_options = {
'batch_size': args.batch_size,
'sequence_length': args.sequence_length,
'validation_frequency': args.validation_frequency,
'patience': args.patience,
'stopping_criterion': args.stopping_criterion,
'max_epochs': args.max_epochs,
'min_epochs': args.min_epochs,
'max_annealing_count': args.max_annealing_count
}
optimization_options = {
'method': args.optimization_method,
'epsilon': args.numerical_stability_term,
'gradient_decay_rate': args.gradient_decay_rate,
'sqr_gradient_decay_rate': args.sqr_gradient_decay_rate,
'learning_rate': args.learning_rate,
'weights': weights,
'momentum': args.momentum,
'max_gradient_norm': args.gradient_normalization,
'num_noise_samples': args.num_noise_samples,
'noise_sharing': args.noise_sharing,
}
log_options(training_options, optimization_options, args)
logging.info("Creating trainer.")
trainer = Trainer(training_options, vocabulary, args.training_set,
args.sampling)
trainer.set_logging(args.log_interval)
logging.info("Building neural network.")
if args.architecture == 'lstm300' or args.architecture == 'lstm1500':
architecture = Architecture.from_package(args.architecture)
else:
with open(args.architecture, 'rt', encoding='utf-8') as arch_file:
architecture = Architecture.from_description(arch_file)
default_device = get_default_device(args.default_device)
network = Network(architecture,
vocabulary,
trainer.class_prior_probs,
default_device=default_device,
profile=args.profile)
network.set_sampling(args.noise_distribution, args.noise_dampening,
args.noise_sharing)
logging.info("Building optimizer.")
exclude_id = vocabulary.word_to_id['<unk>'] if args.exclude_unk \
else None
epsilon = args.numerical_stability_term
if args.cost == 'cross-entropy':
cost_function = CrossEntropyCost(network, exclude_id,
args.l1_regularization,
args.l2_regularization, epsilon)
elif args.cost == 'nce':
cost_function = NCECost(network, exclude_id,
args.l1_regularization,
args.l2_regularization, epsilon)
else:
assert args.cost == 'blackout'
cost_function = BlackoutCost(network, exclude_id,
args.l1_regularization,
args.l2_regularization, epsilon)
try:
optimizer = create_optimizer(optimization_options,
network,
cost_function,
profile=args.profile)
except theano.gradient.DisconnectedInputError as e:
print("Cannot train the neural network because some of the "
"parameters are disconnected from the output. Make sure all "
"the layers are correctly connected in the network "
"architecture. The error message was: `{}´".format(e))
if args.print_graph:
print("Cost function computation graph:")
theano.printing.debugprint(optimizer.gradient_update_function)
trainer.initialize(network, state, optimizer, args.load_and_train)
if args.validation_file is not None:
logging.info("Building text scorer for cross-validation.")
scorer = TextScorer(network,
use_shortlist=True,
exclude_unk=args.exclude_unk,
profile=args.profile)
logging.info("Validation text: %s", args.validation_file.name)
validation_mmap = mmap.mmap(args.validation_file.fileno(),
0,
prot=mmap.PROT_READ)
validation_iter = \
LinearBatchIterator(validation_mmap,
vocabulary,
batch_size=args.batch_size,
max_sequence_length=args.sequence_length,
map_oos_to_unk=False)
trainer.set_validation(validation_iter, scorer)
else:
logging.info("Cross-validation will not be performed.")
validation_iter = None
logging.info("Training neural network.")
trainer.train()
if 'layers' not in state.keys():
print("The model has not been trained. No cross-validations were "
"performed or training did not improve the model.")
elif validation_iter is not None:
network.set_state(state)
perplexity = scorer.compute_perplexity(validation_iter)
print("Best validation set perplexity:", perplexity)