<gh_stars>0
"""A program to train a tensorflow neural net parser from a conll file."""
import os
import os.path
import random
import time
import tensorflow as tf
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging as logging
from google.protobuf import text_format
from syntaxnet.ops import gen_parser_ops
from syntaxnet import task_spec_pb2
from syntaxnet import sentence_pb2
from dragnn.protos import spec_pb2
from dragnn.python import evaluation
from dragnn.python import graph_builder
from dragnn.python import lexicon
from dragnn.python import sentence_io
from dragnn.python import spec_builder
from dragnn.python import trainer_lib
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string("tf_master", "", "TensorFlow execution engine to connect to.")
flags.DEFINE_string("resource_path", "", "Path to constructed resources.")
flags.DEFINE_string("tensorboard_dir", "", "Directory for TensorBoard logs output.")
flags.DEFINE_string("checkpoint_filename", "", "Filename to save the best checkpoint to.")
flags.DEFINE_string("training_corpus_path", "", "Path to training data.")
flags.DEFINE_string("dev_corpus_path", "", "Path to development set data.")
flags.DEFINE_bool("compute_lexicon", False, "")
flags.DEFINE_bool("projectivize_training_set", True, "")
flags.DEFINE_integer("batch_size", 4, "Batch size.")
flags.DEFINE_integer("report_every", 200, "Report cost and training accuracy every this many steps.")
def main(unused_argv):
	logging.set_verbosity(logging.INFO)
	if not gfile.IsDirectory(FLAGS.resource_path):
		gfile.MakeDirs(FLAGS.resource_path)
	if FLAGS.compute_lexicon:
		logging.info("Computing lexicon...")
		lexicon.build_lexicon(FLAGS.resource_path, FLAGS.training_corpus_path)
	char2word = spec_builder.ComponentSpecBuilder("char_lstm")
	char2word.set_network_unit(name="wrapped_units.LayerNormBasicLSTMNetwork", hidden_layer_sizes="256")
	char2word.set_transition_system(name="char-shift-only", left_to_right="true")
	char2word.add_fixed_feature(name="chars", fml="char-input.text-char", embedding_dim=16)
	char2word.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	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_link(source=char2word, fml="input.last-char-focus", embedding_dim=32)
	lookahead.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	tagger = spec_builder.ComponentSpecBuilder("tagger")
	tagger.set_network_unit(name="wrapped_units.LayerNormBasicLSTMNetwork", hidden_layer_sizes="256")
	tagger.set_transition_system(name="tagger")
	tagger.add_token_link(source=lookahead, fml="input.focus", embedding_dim=32)
	tagger.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	parser = spec_builder.ComponentSpecBuilder("parser")
	parser.set_network_unit(name="FeedForwardNetwork", hidden_layer_sizes="256", layer_norm_hidden="True")
	parser.set_transition_system(name="arc-standard")
	parser.add_token_link(source=lookahead, fml="input.focus", embedding_dim=32)
	parser.add_token_link(source=tagger, fml="input.focus stack.focus stack(1).focus", embedding_dim=32)
	parser.add_link(source=parser, name="rnn-stack", fml="stack.focus stack(1).focus", source_translator="shift-reduce-step", embedding_dim=32)
	parser.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	master_spec = spec_pb2.MasterSpec()
	master_spec.component.extend([char2word.spec, lookahead.spec, tagger.spec, parser.spec])
	logging.info("Constructed master spec: %s", str(master_spec))
	hyperparam_config = spec_pb2.GridPoint()
	hyperparam_config.decay_steps = 128000
	hyperparam_config.learning_rate = 0.001
	hyperparam_config.learning_method = "adam"
	hyperparam_config.adam_beta1 = 0.9
	hyperparam_config.adam_beta2 = 0.9
	hyperparam_config.adam_eps = 0.0001
	hyperparam_config.gradient_clip_norm = 1
	hyperparam_config.self_norm_alpha = 1.0
	hyperparam_config.use_moving_average = True
	hyperparam_config.dropout_rate = 0.7
	hyperparam_config.seed = 1
	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) == 2
		annotator = builder.add_annotation()
		builder.add_saver()
	training_set = sentence_io.ConllSentenceReader(FLAGS.training_corpus_path, projectivize=FLAGS.projectivize_training_set).corpus()
	dev_set = sentence_io.ConllSentenceReader(FLAGS.dev_corpus_path, projectivize=False).corpus()
	logging.info("Training on %d sentences.", len(training_set))
	logging.info("Tuning on %d sentences.", len(dev_set))
	pretrain_steps = [100, 0]
	tagger_steps = 1000
	train_steps = [tagger_steps, 8 * tagger_steps]
	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:
		sess.run(tf.global_variables_initializer())
		trainer_lib.run_training(sess, trainers, annotator, evaluation.parser_summaries, pretrain_steps, train_steps, training_set, dev_set, dev_set, FLAGS.batch_size, summary_writer, FLAGS.report_every, builder.saver, FLAGS.checkpoint_filename)
if __name__ == "__main__":
	tf.app.run()
"""A program to train a tensorflow neural net parser from a conll file."""
import os
import os.path
import random
import time
import tensorflow as tf
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging as logging
from google.protobuf import text_format
from syntaxnet.ops import gen_parser_ops
from syntaxnet import task_spec_pb2
from syntaxnet import sentence_pb2
from dragnn.protos import spec_pb2
from dragnn.python import evaluation
from dragnn.python import graph_builder
from dragnn.python import lexicon
from dragnn.python import sentence_io
from dragnn.python import spec_builder
from dragnn.python import trainer_lib
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string("tf_master", "", "TensorFlow execution engine to connect to.")
flags.DEFINE_string("resource_path", "", "Path to constructed resources.")
flags.DEFINE_string("tensorboard_dir", "", "Directory for TensorBoard logs output.")
flags.DEFINE_string("checkpoint_filename", "", "Filename to save the best checkpoint to.")
flags.DEFINE_string("training_corpus_path", "", "Path to training data.")
flags.DEFINE_string("dev_corpus_path", "", "Path to development set data.")
flags.DEFINE_bool("compute_lexicon", False, "")
flags.DEFINE_bool("projectivize_training_set", True, "")
flags.DEFINE_integer("batch_size", 4, "Batch size.")
flags.DEFINE_integer("report_every", 200, "Report cost and training accuracy every this many steps.")
def main(unused_argv):
	logging.set_verbosity(logging.INFO)
	if not gfile.IsDirectory(FLAGS.resource_path):
		gfile.MakeDirs(FLAGS.resource_path)
	if FLAGS.compute_lexicon:
		logging.info("Computing lexicon...")
		lexicon.build_lexicon(FLAGS.resource_path, FLAGS.training_corpus_path)
	char2word = spec_builder.ComponentSpecBuilder("char_lstm")
	char2word.set_network_unit(name="wrapped_units.LayerNormBasicLSTMNetwork", hidden_layer_sizes="256")
	char2word.set_transition_system(name="char-shift-only", left_to_right="true")
	char2word.add_fixed_feature(name="chars", fml="char-input.text-char", embedding_dim=16)
	char2word.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	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_link(source=char2word, fml="input.last-char-focus", embedding_dim=32)
	lookahead.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	tagger = spec_builder.ComponentSpecBuilder("tagger")
	tagger.set_network_unit(name="wrapped_units.LayerNormBasicLSTMNetwork", hidden_layer_sizes="256")
	tagger.set_transition_system(name="tagger")
	tagger.add_token_link(source=lookahead, fml="input.focus", embedding_dim=32)
	tagger.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	parser = spec_builder.ComponentSpecBuilder("parser")
	parser.set_network_unit(name="FeedForwardNetwork", hidden_layer_sizes="256", layer_norm_hidden="True")
	parser.set_transition_system(name="arc-standard")
	parser.add_token_link(source=lookahead, fml="input.focus", embedding_dim=32)
	parser.add_token_link(source=tagger, fml="input.focus stack.focus stack(1).focus", embedding_dim=32)
	parser.add_link(source=parser, name="rnn-stack", fml="stack.focus stack(1).focus", source_translator="shift-reduce-step", embedding_dim=32)
	parser.fill_from_resources(FLAGS.resource_path, FLAGS.tf_master)
	master_spec = spec_pb2.MasterSpec()
	master_spec.component.extend([char2word.spec, lookahead.spec, tagger.spec, parser.spec])
	logging.info("Constructed master spec: %s", str(master_spec))
	hyperparam_config = spec_pb2.GridPoint()
	hyperparam_config.decay_steps = 128000
	hyperparam_config.learning_rate = 0.001
	hyperparam_config.learning_method = "adam"
	hyperparam_config.adam_beta1 = 0.9
	hyperparam_config.adam_beta2 = 0.9
	hyperparam_config.adam_eps = 0.0001
	hyperparam_config.gradient_clip_norm = 1
	hyperparam_config.self_norm_alpha = 1.0
	hyperparam_config.use_moving_average = True
	hyperparam_config.dropout_rate = 0.7
	hyperparam_config.seed = 1
	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) == 2
		annotator = builder.add_annotation()
		builder.add_saver()
	training_set = sentence_io.ConllSentenceReader(FLAGS.training_corpus_path, projectivize=FLAGS.projectivize_training_set).corpus()
	dev_set = sentence_io.ConllSentenceReader(FLAGS.dev_corpus_path, projectivize=False).corpus()
	logging.info("Training on %d sentences.", len(training_set))
	logging.info("Tuning on %d sentences.", len(dev_set))
	pretrain_steps = [100, 0]
	tagger_steps = 1000
	train_steps = [tagger_steps, 8 * tagger_steps]
	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:
		sess.run(tf.global_variables_initializer())
		trainer_lib.run_training(sess, trainers, annotator, evaluation.parser_summaries, pretrain_steps, train_steps, training_set, dev_set, dev_set, FLAGS.batch_size, summary_writer, FLAGS.report_every, builder.saver, FLAGS.checkpoint_filename)
if __name__ == "__main__":
	tf.app.run()
A program to train a tensorflow neural net parser from a conll file.