Python graph_builder.MasterBuilder方法代码示例

# 需要导入模块: from dragnn.python import graph_builder [as 别名]
# 或者: from dragnn.python.graph_builder import MasterBuilder [as 别名]
def RunTraining(self, hyperparam_config):
    master_spec = self.LoadSpec('master_spec_link.textproto')

    self.assertTrue(isinstance(hyperparam_config, spec_pb2.GridPoint))
    gold_doc = sentence_pb2.Sentence()
    text_format.Parse(_DUMMY_GOLD_SENTENCE, gold_doc)
    gold_doc_2 = sentence_pb2.Sentence()
    text_format.Parse(_DUMMY_GOLD_SENTENCE_2, gold_doc_2)
    reader_strings = [
        gold_doc.SerializeToString(), gold_doc_2.SerializeToString()
    ]
    tf.logging.info('Generating graph with config: %s', hyperparam_config)
    with tf.Graph().as_default():
      builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)

      target = spec_pb2.TrainTarget()
      target.name = 'testTraining-all'
      train = builder.add_training_from_config(target)
      with self.test_session() as sess:
        logging.info('Initializing')
        sess.run(tf.global_variables_initializer())

        # Run one iteration of training and verify nothing crashes.
        logging.info('Training')
        sess.run(train['run'], feed_dict={train['input_batch']: reader_strings}) 

# 需要导入模块: from dragnn.python import graph_builder [as 别名]
# 或者: from dragnn.python.graph_builder import MasterBuilder [as 别名]
def getBuilderAndTarget(
      self, test_name, master_spec_path='simple_parser_master_spec.textproto'):
    """Generates a MasterBuilder and TrainTarget based on a simple spec."""
    master_spec = self.LoadSpec(master_spec_path)
    hyperparam_config = spec_pb2.GridPoint()
    target = spec_pb2.TrainTarget()
    target.name = 'test-%s-train' % test_name
    target.component_weights.extend([0] * len(master_spec.component))
    target.component_weights[-1] = 1.0
    target.unroll_using_oracle.extend([False] * len(master_spec.component))
    target.unroll_using_oracle[-1] = True
    builder = graph_builder.MasterBuilder(
        master_spec, hyperparam_config, pool_scope=test_name)
    return builder, target 

# 需要导入模块: from dragnn.python import graph_builder [as 别名]
# 或者: from dragnn.python.graph_builder import MasterBuilder [as 别名]
def RunTraining(self, hyperparam_config):
    master_spec = self.LoadSpec('master_spec_link.textproto')

    self.assertTrue(isinstance(hyperparam_config, spec_pb2.GridPoint))
    gold_doc = sentence_pb2.Sentence()
    text_format.Parse(_DUMMY_GOLD_SENTENCE, gold_doc)
    gold_doc_2 = sentence_pb2.Sentence()
    text_format.Parse(_DUMMY_GOLD_SENTENCE_2, gold_doc_2)
    reader_strings = [
        gold_doc.SerializeToString(),
        gold_doc_2.SerializeToString()
    ]
    tf.logging.info('Generating graph with config: %s', hyperparam_config)
    with tf.Graph().as_default():
      builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)

      target = spec_pb2.TrainTarget()
      target.name = 'testTraining-all'
      train = builder.add_training_from_config(target)
      with self.test_session() as sess:
        logging.info('Initializing')
        sess.run(tf.global_variables_initializer())

        # Run one iteration of training and verify nothing crashes.
        logging.info('Training')
        sess.run(train['run'], feed_dict={train['input_batch']: reader_strings}) 

# 需要导入模块: from dragnn.python import graph_builder [as 别名]
# 或者: from dragnn.python.graph_builder import MasterBuilder [as 别名]
def load_model(base_dir, master_spec_name, checkpoint_name):
    """
    Function to load the syntaxnet models. Highly specific to the tutorial
    format right now.
    """
    # Read the master spec
    master_spec = spec_pb2.MasterSpec()
    with open(os.path.join(base_dir, master_spec_name), "r") as f:
        text_format.Merge(f.read(), master_spec)
    spec_builder.complete_master_spec(master_spec, None, base_dir)
    logging.set_verbosity(logging.WARN)  # Turn off TensorFlow spam.

    # Initialize a graph
    graph = tf.Graph()
    with graph.as_default():
        hyperparam_config = spec_pb2.GridPoint()
        builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)
        # This is the component that will annotate test sentences.
        annotator = builder.add_annotation(enable_tracing=True)
        builder.add_saver()  # "Savers" can save and load models; here, we're only going to load.

    sess = tf.Session(graph=graph)
    with graph.as_default():
        #sess.run(tf.global_variables_initializer())
        #sess.run('save/restore_all', {'save/Const:0': os.path.join(base_dir, checkpoint_name)})
        builder.saver.restore(sess, os.path.join(base_dir, checkpoint_name))

    def annotate_sentence(sentence):
        with graph.as_default():
            return sess.run([annotator['annotations'], annotator['traces']],
                            feed_dict={annotator['input_batch']: [sentence]})
    return annotate_sentence 

# 需要导入模块: from dragnn.python import graph_builder [as 别名]
# 或者: from dragnn.python.graph_builder import MasterBuilder [as 别名]
def load_model(self,base_dir, master_spec_name, checkpoint_name):
        # Read the master spec
        master_spec = spec_pb2.MasterSpec()
        with open(os.path.join(base_dir, master_spec_name), "r") as f:
            text_format.Merge(f.read(), master_spec)
        spec_builder.complete_master_spec(master_spec, None, base_dir)
        logging.set_verbosity(logging.WARN)  # Turn off TensorFlow spam.

        # Initialize a graph
        graph = tf.Graph()
        with graph.as_default():
            hyperparam_config = spec_pb2.GridPoint()
            builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)
            # This is the component that will annotate test sentences.
            annotator = builder.add_annotation(enable_tracing=True)
            builder.add_saver()  # "Savers" can save and load models; here, we're only going to load.

        sess = tf.Session(graph=graph)
        with graph.as_default():
            # sess.run(tf.global_variables_initializer())
            # sess.run('save/restore_all', {'save/Const:0': os.path.join(base_dir, checkpoint_name)})
            builder.saver.restore(sess, os.path.join(base_dir, checkpoint_name))

        def annotate_sentence(sentence):
            with graph.as_default():
                return sess.run([annotator['annotations'], annotator['traces']],
                                feed_dict={annotator['input_batch']: [sentence]})

        return annotate_sentence 

# 需要导入模块: from dragnn.python import graph_builder [as 别名]
# 或者: from dragnn.python.graph_builder import MasterBuilder [as 别名]
def main(argv):
  del argv  # unused
  # Constructs lexical resources for SyntaxNet in the given resource path, from
  # the training data.
  lexicon.build_lexicon(
      lexicon_dir,
      training_sentence,
      training_corpus_format='sentence-prototext')

  # Construct the ComponentSpec for tagging. This is a simple left-to-right RNN
  # sequence tagger.
  tagger = spec_builder.ComponentSpecBuilder('tagger')
  tagger.set_network_unit(name='FeedForwardNetwork', hidden_layer_sizes='256')
  tagger.set_transition_system(name='tagger')
  tagger.add_fixed_feature(name='words', fml='input.word', embedding_dim=64)
  tagger.add_rnn_link(embedding_dim=-1)
  tagger.fill_from_resources(lexicon_dir)

  master_spec = spec_pb2.MasterSpec()
  master_spec.component.extend([tagger.spec])

  hyperparam_config = spec_pb2.GridPoint()

  # Build the TensorFlow graph.
  graph = tf.Graph()
  with graph.as_default():
    builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)

    target = spec_pb2.TrainTarget()
    target.name = 'all'
    target.unroll_using_oracle.extend([True])
    dry_run = builder.add_training_from_config(target, trace_only=True)

  # Read in serialized protos from training data.
  sentence = sentence_pb2.Sentence()
  text_format.Merge(open(training_sentence).read(), sentence)
  training_set = [sentence.SerializeToString()]

  with tf.Session(graph=graph) as sess:
    # Make sure to re-initialize all underlying state.
    sess.run(tf.initialize_all_variables())
    traces = sess.run(
        dry_run['traces'], feed_dict={dry_run['input_batch']: training_set})

  with open('dragnn_tutorial_1.html', 'w') as f:
    f.write(visualization.trace_html(traces[0], height='300px').encode('utf-8'))