def test_from_corpus(self):
self.sentences1_file.seek(0)
vocabulary = Vocabulary.from_corpus([self.sentences1_file])
self.assertEqual(vocabulary.num_words(), 10 + 3)
self.assertEqual(vocabulary.num_classes(), 10 + 3)
self.sentences1_file.seek(0)
self.sentences2_file.seek(0)
vocabulary = Vocabulary.from_corpus([self.sentences1_file,
self.sentences2_file],
3)
self.assertEqual(vocabulary.num_words(), 10 + 3)
self.assertEqual(vocabulary.num_classes(), 3 + 3)
sos_id = vocabulary.word_to_id['<s>']
eos_id = vocabulary.word_to_id['</s>']
unk_id = vocabulary.word_to_id['<unk>']
self.assertEqual(sos_id, 10)
self.assertEqual(eos_id, 11)
self.assertEqual(unk_id, 12)
self.assertEqual(vocabulary.word_id_to_class_id[sos_id], 3)
self.assertEqual(vocabulary.word_id_to_class_id[eos_id], 4)
self.assertEqual(vocabulary.word_id_to_class_id[unk_id], 5)
word_ids = set()
class_ids = set()
for word in vocabulary.words():
if not word.startswith('<'):
word_id = vocabulary.word_to_id[word]
word_ids.add(word_id)
class_ids.add(vocabulary.word_id_to_class_id[word_id])
self.assertEqual(word_ids, set(range(10)))
self.assertEqual(class_ids, set(range(3)))
def test_from_corpus(self):
self.sentences1_file.seek(0)
vocabulary = Vocabulary.from_corpus([self.sentences1_file])
self.assertEqual(vocabulary.num_words(), 10 + 3)
self.assertEqual(vocabulary.num_classes(), 10 + 3)
self.sentences1_file.seek(0)
self.sentences2_file.seek(0)
vocabulary = Vocabulary.from_corpus([self.sentences1_file,
self.sentences2_file],
3)
self.assertEqual(vocabulary.num_words(), 10 + 3)
self.assertEqual(vocabulary.num_classes(), 3 + 3)
sos_id = vocabulary.word_to_id['<s>']
eos_id = vocabulary.word_to_id['</s>']
unk_id = vocabulary.word_to_id['<unk>']
self.assertEqual(sos_id, 10)
self.assertEqual(eos_id, 11)
self.assertEqual(unk_id, 12)
self.assertEqual(vocabulary.word_id_to_class_id[sos_id], 3)
self.assertEqual(vocabulary.word_id_to_class_id[eos_id], 4)
self.assertEqual(vocabulary.word_id_to_class_id[unk_id], 5)
word_ids = set()
class_ids = set()
for word in vocabulary.words():
if not word.startswith('<'):
word_id = vocabulary.word_to_id[word]
word_ids.add(word_id)
class_ids.add(vocabulary.word_id_to_class_id[word_id])
self.assertEqual(word_ids, set(range(10)))
self.assertEqual(class_ids, set(range(3)))
def setUp(self):
script_path = os.path.dirname(os.path.realpath(__file__))
sentences_path = os.path.join(script_path, 'sentences.txt')
with open(sentences_path) as sentences_file:
self.vocabulary = Vocabulary.from_corpus(sentences_file)
sentences_file.seek(0)
self.statistics = WordStatistics([sentences_file], self.vocabulary)
def main():
parser = argparse.ArgumentParser(prog='wctool')
argument_group = parser.add_argument_group("files")
argument_group.add_argument(
'--training-set',
metavar='FILE',
type=TextFileType('r'),
nargs='+',
required=True,
help='text or .gz files containing training data (one sentence per '
'line)')
argument_group.add_argument(
'--vocabulary',
metavar='FILE',
type=TextFileType('r'),
default=None,
help='text or .gz file containing a list of words to include in class '
'forming, and possibly their initial classes')
argument_group.add_argument(
'--vocabulary-format',
metavar='FORMAT',
type=str,
default='words',
help='vocabulary format, one of "words" (one word per line, default), '
'"classes" (word and class ID per line), "srilm-classes" (class '
'name, membership probability, and word per line)')
argument_group.add_argument(
'--output-file',
metavar='FILE',
type=TextFileType('w'),
default='-',
help='where to write the word classes (default stdout)')
argument_group.add_argument(
'--output-format',
metavar='FORMAT',
type=str,
default='srilm-classes',
help='format of the output file, one of "classes" (word and class ID '
'per line), "srilm-classes" (default; class name, membership '
'probability, and word per line)')
argument_group.add_argument(
'--output-frequency',
metavar='N',
type=int,
default='1',
help='save classes N times per optimization iteration (default 1)')
argument_group = parser.add_argument_group("optimization")
argument_group.add_argument(
'--num-classes',
metavar='N',
type=int,
default=2000,
help='number of classes to form, if vocabulary is not specified '
'(default 2000)')
argument_group.add_argument(
'--method',
metavar='NAME',
type=str,
default='bigram-theano',
help='method for creating word classes, one of "bigram-theano", '
'"bigram-numpy" (default "bigram-theano")')
argument_group = parser.add_argument_group("logging and debugging")
argument_group.add_argument(
'--log-file',
metavar='FILE',
type=str,
default='-',
help='path where to write log file (default is standard output)')
argument_group.add_argument(
'--log-level',
metavar='LEVEL',
type=str,
default='info',
help='minimum level of events to log, one of "debug", "info", "warn" '
'(default "info")')
argument_group.add_argument(
'--log-interval',
metavar='N',
type=int,
default=1000,
help='print statistics after every Nth word; quiet if less than one '
'(default 1000)')
args = parser.parse_args()
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
raise ValueError("Invalid logging level requested: " + args.log_level)
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.vocabulary is None:
vocabulary = Vocabulary.from_corpus(args.training_set,
args.num_classes)
for subset_file in args.training_set:
subset_file.seek(0)
else:
vocabulary = Vocabulary.from_file(args.vocabulary,
args.vocabulary_format)
print("Number of words in vocabulary:", vocabulary.num_words())
print("Number of word classes:", vocabulary.num_classes())
print("Number of normal word classes:", vocabulary.num_normal_classes)
logging.info("Reading word unigram and bigram statistics.")
statistics = WordStatistics(args.training_set, vocabulary)
if args.method == 'bigram-theano':
optimizer = TheanoBigramOptimizer(statistics, vocabulary)
elif args.method == 'bigram-numpy':
optimizer = NumpyBigramOptimizer(statistics, vocabulary)
else:
raise ValueError("Invalid method requested: " + args.method)
iteration = 1
while True:
logging.info("Starting iteration %d.", iteration)
num_words = 0
num_moves = 0
for word in vocabulary.words():
start_time = time()
num_words += 1
if optimizer.move_to_best_class(word):
num_moves += 1
duration = time() - start_time
if (args.log_interval >= 1) and \
(num_words % args.log_interval == 0):
logging.info(
"[%d] (%.1f %%) of iteration %d -- moves = %d, cost = %.2f, duration = %.1f ms",
num_words, num_words / vocabulary.num_words() * 100,
iteration, num_moves, optimizer.log_likelihood(),
duration * 100)
if is_scheduled(num_words, args.output_frequency,
vocabulary.num_words()):
save(optimizer, args.output_file, args.output_format)
if num_moves == 0:
break
iteration += 1
logging.info("Optimization finished.")
save(optimizer, args.output_file, args.output_format)
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):
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'
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())
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(vocabulary, architecture, profile=args.profile)
sys.stdout.flush()
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
print("Building text scorer.")
scorer = TextScorer(network, ignore_unk, unk_penalty, args.profile)
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)
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,
'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))
training_options = {
'strategy': args.training_strategy,
'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))
print("Building neural network trainer.")
sys.stdout.flush()
if len(args.sampling) > len(args.training_set):
print("You specified more sampling coefficients than training "
"files.")
sys.exit(1)
trainer = create_trainer(
training_options, optimization_options,
network, vocabulary, scorer,
args.training_set, args.sampling, validation_iter,
state, args.profile)
trainer.set_logging(args.log_interval)
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.")
else:
network.set_state(state)
perplexity = scorer.compute_perplexity(validation_iter)
print("Best validation set perplexity:", perplexity)
def main():
parser = argparse.ArgumentParser(prog='wctool')
argument_group = parser.add_argument_group("files")
argument_group.add_argument(
'--training-set', metavar='FILE', type=TextFileType('r'),
nargs='+', required=True,
help='text or .gz files containing training data (one sentence per '
'line)')
argument_group.add_argument(
'--vocabulary', metavar='FILE', type=TextFileType('r'), default=None,
help='text or .gz file containing a list of words to include in class '
'forming, and possibly their initial classes')
argument_group.add_argument(
'--vocabulary-format', metavar='FORMAT', type=str, default='words',
help='vocabulary format, one of "words" (one word per line, default), '
'"classes" (word and class ID per line), "srilm-classes" (class '
'name, membership probability, and word per line)')
argument_group.add_argument(
'--output-file', metavar='FILE', type=TextFileType('w'), default='-',
help='where to write the word classes (default stdout)')
argument_group.add_argument(
'--output-format', metavar='FORMAT', type=str, default='srilm-classes',
help='format of the output file, one of "classes" (word and class ID '
'per line), "srilm-classes" (default; class name, membership '
'probability, and word per line)')
argument_group.add_argument(
'--output-frequency', metavar='N', type=int, default='1',
help='save classes N times per optimization iteration (default 1)')
argument_group = parser.add_argument_group("optimization")
argument_group.add_argument(
'--num-classes', metavar='N', type=int, default=2000,
help='number of classes to form, if vocabulary is not specified '
'(default 2000)')
argument_group.add_argument(
'--method', metavar='NAME', type=str, default='bigram-theano',
help='method for creating word classes, one of "bigram-theano", '
'"bigram-numpy" (default "bigram-theano")')
argument_group = parser.add_argument_group("logging and debugging")
argument_group.add_argument(
'--log-file', metavar='FILE', type=str, default='-',
help='path where to write log file (default is standard output)')
argument_group.add_argument(
'--log-level', metavar='LEVEL', type=str, default='info',
help='minimum level of events to log, one of "debug", "info", "warn" '
'(default "info")')
argument_group.add_argument(
'--log-interval', metavar='N', type=int, default=1000,
help='print statistics after every Nth word; quiet if less than one '
'(default 1000)')
args = parser.parse_args()
log_file = args.log_file
log_level = getattr(logging, args.log_level.upper(), None)
if not isinstance(log_level, int):
raise ValueError("Invalid logging level requested: " + args.log_level)
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.vocabulary is None:
vocabulary = Vocabulary.from_corpus(args.training_set,
args.num_classes)
for subset_file in args.training_set:
subset_file.seek(0)
else:
vocabulary = Vocabulary.from_file(args.vocabulary,
args.vocabulary_format)
print("Number of words in vocabulary:", vocabulary.num_words())
print("Number of word classes:", vocabulary.num_classes())
print("Number of normal word classes:", vocabulary.num_normal_classes)
logging.info("Reading word unigram and bigram statistics.")
statistics = WordStatistics(args.training_set, vocabulary)
if args.method == 'bigram-theano':
optimizer = TheanoBigramOptimizer(statistics, vocabulary)
elif args.method == 'bigram-numpy':
optimizer = NumpyBigramOptimizer(statistics, vocabulary)
else:
raise ValueError("Invalid method requested: " + args.method)
iteration = 1
while True:
logging.info("Starting iteration %d.", iteration)
num_words = 0
num_moves = 0
for word in vocabulary.words():
start_time = time()
num_words += 1
if optimizer.move_to_best_class(word):
num_moves += 1
duration = time() - start_time
if (args.log_interval >= 1) and \
(num_words % args.log_interval == 0):
logging.info("[%d] (%.1f %%) of iteration %d -- moves = %d, cost = %.2f, duration = %.1f ms",
num_words,
num_words / vocabulary.num_words() * 100,
iteration,
num_moves,
optimizer.log_likelihood(),
duration * 100)
if is_scheduled(num_words,
args.output_frequency,
vocabulary.num_words()):
save(optimizer, args.output_file, args.output_format)
if num_moves == 0:
break
iteration += 1
logging.info("Optimization finished.")
save(optimizer, args.output_file, args.output_format)
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):
raise ValueError("Invalid logging level requested: " + args.log_level)
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'
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())
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(vocabulary, architecture, profile=args.profile)
sys.stdout.flush()
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
print("Building text scorer.")
scorer = TextScorer(network, ignore_unk, unk_penalty, args.profile)
validation_mmap = mmap.mmap(args.validation_file.fileno(),
0,
prot=mmap.PROT_READ)
validation_iter = LinearBatchIterator(validation_mmap,
vocabulary,
batch_size=32)
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,
'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))
training_options = {
'strategy': args.training_strategy,
'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))
print("Building neural network trainer.")
sys.stdout.flush()
if len(args.sampling) > len(args.training_set):
print("You specified more sampling coefficients than training "
"files.")
sys.exit(1)
trainer = create_trainer(
training_options, optimization_options,
network, vocabulary, scorer,
args.training_set, args.sampling, validation_iter,
state, args.profile)
trainer.set_logging(args.log_interval)
print("Training neural network.")
sys.stdout.flush()
trainer.run()
if not state.keys():
print("The model has not been trained.")
else:
network.set_state(state)
perplexity = scorer.compute_perplexity(validation_iter)
print("Best validation set perplexity:", perplexity)