def main(unused_argv):
logging.set_verbosity(logging.INFO)
if not gfile.IsDirectory(FLAGS.resource_path):
gfile.MakeDirs(FLAGS.resource_path)
# Constructs lexical resources for SyntaxNet in the given resource path, from
# the training data.
if FLAGS.compute_lexicon:
logging.info('Computing lexicon...')
lexicon.build_lexicon(FLAGS.resource_path, FLAGS.training_corpus_path)
# Construct the "lookahead" ComponentSpec. This is a simple right-to-left RNN
# sequence model, which encodes the context to the right of each token. It has
# no loss except for the downstream components.
lookahead = spec_builder.ComponentSpecBuilder('lookahead')
lookahead.set_network_unit(name='wrapped_units.LayerNormBasicLSTMNetwork',
hidden_layer_sizes='256')
lookahead.set_transition_system(name='shift-only', left_to_right='false')
lookahead.add_fixed_feature(name='char',
fml='input(-1).char input.char input(1).char',
embedding_dim=32)
lookahead.add_fixed_feature(name='char-bigram',
fml='input.char-bigram',
embedding_dim=32)
lookahead.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
# Construct the ComponentSpec for segmentation.
segmenter = spec_builder.ComponentSpecBuilder('segmenter')
segmenter.set_network_unit(name='wrapped_units.LayerNormBasicLSTMNetwork',
hidden_layer_sizes='128')
segmenter.set_transition_system(name='binary-segment-transitions')
segmenter.add_token_link(source=lookahead,
fml='input.focus stack.focus',
embedding_dim=64)
segmenter.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
# Build and write master_spec.
master_spec = spec_pb2.MasterSpec()
master_spec.component.extend([lookahead.spec, segmenter.spec])
logging.info('Constructed master spec: %s', str(master_spec))
with gfile.GFile(FLAGS.resource_path + '/master_spec', 'w') as f:
f.write(str(master_spec).encode('utf-8'))
hyperparam_config = spec_pb2.GridPoint()
try:
text_format.Parse(FLAGS.hyperparams, hyperparam_config)
except text_format.ParseError:
text_format.Parse(base64.b64decode(FLAGS.hyperparams),
hyperparam_config)
# Build the TensorFlow graph.
graph = tf.Graph()
with graph.as_default():
builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)
component_targets = spec_builder.default_targets_from_spec(master_spec)
trainers = [
builder.add_training_from_config(target)
for target in component_targets
]
assert len(trainers) == 1
annotator = builder.add_annotation()
builder.add_saver()
# Read in serialized protos from training data.
training_set = ConllSentenceReader(FLAGS.training_corpus_path,
projectivize=False).corpus()
dev_set = ConllSentenceReader(FLAGS.dev_corpus_path,
projectivize=False).corpus()
# Convert word-based docs to char-based documents for segmentation training
# and evaluation.
with tf.Session(graph=tf.Graph()) as tmp_session:
char_training_set_op = gen_parser_ops.segmenter_training_data_constructor(
training_set)
char_dev_set_op = gen_parser_ops.char_token_generator(dev_set)
char_training_set = tmp_session.run(char_training_set_op)
char_dev_set = tmp_session.run(char_dev_set_op)
# Ready to train!
logging.info('Training on %d sentences.', len(training_set))
logging.info('Tuning on %d sentences.', len(dev_set))
pretrain_steps = [0]
train_steps = [FLAGS.num_epochs * len(training_set)]
tf.logging.info('Creating TensorFlow checkpoint dir...')
gfile.MakeDirs(os.path.dirname(FLAGS.checkpoint_filename))
summary_writer = trainer_lib.get_summary_writer(FLAGS.tensorboard_dir)
with tf.Session(FLAGS.tf_master, graph=graph) as sess:
# Make sure to re-initialize all underlying state.
sess.run(tf.global_variables_initializer())
trainer_lib.run_training(
sess, trainers, annotator, evaluation.segmentation_summaries,
pretrain_steps, train_steps, char_training_set, char_dev_set,
dev_set, FLAGS.batch_size, summary_writer, FLAGS.report_every,
builder.saver, FLAGS.checkpoint_filename)
def main(unused_argv):
logging.set_verbosity(logging.INFO)
if not gfile.IsDirectory(FLAGS.resource_path):
gfile.MakeDirs(FLAGS.resource_path)
# Constructs lexical resources for SyntaxNet in the given resource path, from
# the training data.
if FLAGS.compute_lexicon:
logging.info('Computing lexicon...')
lexicon.build_lexicon(FLAGS.resource_path, FLAGS.training_corpus_path)
# Construct the "lookahead" ComponentSpec. This is a simple right-to-left RNN
# sequence model, which encodes the context to the right of each token. It has
# no loss except for the downstream components.
lookahead = spec_builder.ComponentSpecBuilder('lookahead')
lookahead.set_network_unit(
name='wrapped_units.LayerNormBasicLSTMNetwork', hidden_layer_sizes='256')
lookahead.set_transition_system(name='shift-only', left_to_right='false')
lookahead.add_fixed_feature(name='char',
fml='input(-1).char input.char input(1).char',
embedding_dim=32)
lookahead.add_fixed_feature(name='char-bigram',
fml='input.char-bigram',
embedding_dim=32)
lookahead.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
# Construct the ComponentSpec for segmentation.
segmenter = spec_builder.ComponentSpecBuilder('segmenter')
segmenter.set_network_unit(
name='wrapped_units.LayerNormBasicLSTMNetwork', hidden_layer_sizes='128')
segmenter.set_transition_system(name='binary-segment-transitions')
segmenter.add_token_link(
source=lookahead, fml='input.focus stack.focus',
embedding_dim=64)
segmenter.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
# Build and write master_spec.
master_spec = spec_pb2.MasterSpec()
master_spec.component.extend([lookahead.spec, segmenter.spec])
logging.info('Constructed master spec: %s', str(master_spec))
with gfile.GFile(FLAGS.resource_path + '/master_spec', 'w') as f:
f.write(str(master_spec).encode('utf-8'))
hyperparam_config = spec_pb2.GridPoint()
try:
text_format.Parse(FLAGS.hyperparams, hyperparam_config)
except text_format.ParseError:
text_format.Parse(base64.b64decode(FLAGS.hyperparams), hyperparam_config)
# Build the TensorFlow graph.
graph = tf.Graph()
with graph.as_default():
builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)
component_targets = spec_builder.default_targets_from_spec(master_spec)
trainers = [
builder.add_training_from_config(target) for target in component_targets
]
assert len(trainers) == 1
annotator = builder.add_annotation()
builder.add_saver()
# Read in serialized protos from training data.
training_set = ConllSentenceReader(
FLAGS.training_corpus_path, projectivize=False).corpus()
dev_set = ConllSentenceReader(
FLAGS.dev_corpus_path, projectivize=False).corpus()
# Convert word-based docs to char-based documents for segmentation training
# and evaluation.
with tf.Session(graph=tf.Graph()) as tmp_session:
char_training_set_op = gen_parser_ops.segmenter_training_data_constructor(
training_set)
char_dev_set_op = gen_parser_ops.char_token_generator(dev_set)
char_training_set = tmp_session.run(char_training_set_op)
char_dev_set = tmp_session.run(char_dev_set_op)
# Ready to train!
logging.info('Training on %d sentences.', len(training_set))
logging.info('Tuning on %d sentences.', len(dev_set))
pretrain_steps = [0]
train_steps = [FLAGS.num_epochs * len(training_set)]
tf.logging.info('Creating TensorFlow checkpoint dir...')
gfile.MakeDirs(os.path.dirname(FLAGS.checkpoint_filename))
summary_writer = trainer_lib.get_summary_writer(FLAGS.tensorboard_dir)
with tf.Session(FLAGS.tf_master, graph=graph) as sess:
# Make sure to re-initialize all underlying state.
sess.run(tf.global_variables_initializer())
trainer_lib.run_training(
sess, trainers, annotator, evaluation.segmentation_summaries,
pretrain_steps, train_steps, char_training_set, char_dev_set, dev_set,
FLAGS.batch_size, summary_writer, FLAGS.report_every, builder.saver,
FLAGS.checkpoint_filename)
def _convert_to_char_corpus(corpus):
"""Converts the word-based |corpus| into a char-based corpus."""
with tf.Session(graph=tf.Graph()) as tmp_session:
conversion_op = gen_parser_ops.segmenter_training_data_constructor(
corpus)
return tmp_session.run(conversion_op)
def _convert_to_char_corpus(corpus):
"""Converts the word-based |corpus| into a char-based corpus."""
with tf.Session(graph=tf.Graph()) as tmp_session:
conversion_op = gen_parser_ops.segmenter_training_data_constructor(corpus)
return tmp_session.run(conversion_op)
