def create_config():
c = Config("", "-m", "test")
c.update({"verbose": 2, "timeout": 1, "embedding_layer_dim": 1, "ner_dim": 1, "action_dim": 1, "lemma_dim": 1,
"max_words_external": 3, "word_dim_external": 1, "word_dim": 1, "max_words": 3, "max_lemmas": 3,
"max_tags": 3, "max_pos": 3, "max_deps": 3, "max_edge_labels": 3, "max_puncts": 3, "max_action_types": 3,
"max_ner_types": 3, "edge_label_dim": 1, "tag_dim": 1, "pos_dim": 1, "dep_dim": 1, "optimizer": "sgd",
"output_dim": 1, "layer_dim": 2, "layers": 3, "lstm_layer_dim": 2, "lstm_layers": 3,
"max_action_ratio": 10, "update_word_vectors": False, "copy_shared": None})
c.update_hyperparams(shared={"lstm_layer_dim": 2, "lstm_layers": 1}, ucca={"word_dim": 2},
amr={"max_node_labels": 3, "max_node_categories": 3,
"node_label_dim": 1, "node_category_dim": 1})
return c
def __init__(self):
config = Config()
setting = Settings(*('implicit'))
config.update(setting.dict())
config.set_format("ucca")
self.feature_extractor = DenseFeatureExtractor(
OrderedDict(),
indexed=config.args.classifier != 'mlp',
hierarchical=False,
node_dropout=config.args.node_dropout,
omit_features=config.args.omit_features)
self.sess = tf.Session()
saver = tf.train.import_meta_graph(glob('env_r_model-*.meta')[0])
saver.restore(self.sess, tf.train.latest_checkpoint('./'))
graph = tf.get_default_graph()
self.x = graph.get_tensor_by_name("Placeholder:0")
self.y = graph.get_tensor_by_name("dense_2/BiasAdd:0")
self.length = None
def config():
c = Config("", "-m", "test")
c.update({"no_node_labels": True, "evaluate": True, "minibatch_size": 50})
c.update_hyperparams(shared={"layer_dim": 50})
return c
def config():
c = Config("", "-m", "test")
c.update({"no_node_labels": True, "evaluate": True, "minibatch_size": 50})
c.update_hyperparams(shared={"layer_dim": 50})
return c
def produce_oracle(filename, feature_extractor):
passage = load_passage(filename)
sys.stdout.write('.')
sys.stdout.flush()
#store_sequence_to = "data/oracles/%s/%s.txt" % (cat, basename(filename))#, setting.suffix())
#with open(store_sequence_to, "w", encoding="utf-8") as f:
# for i, action in enumerate(gen_actions(passage, feature_extractor)):
# pass#print(action, file=f)
for _ in gen_actions(passage, feature_extractor):
pass
if __name__ == "__main__":
config = Config()
setting = Settings(*('implicit'))
config.update(setting.dict())
config.set_format("ucca")
feature_extractor = DenseFeatureExtractor(
OrderedDict(),
indexed=config.args.classifier != 'mlp',
hierarchical=False,
node_dropout=config.args.node_dropout,
omit_features=config.args.omit_features)
filenames = passage_files(sys.argv[1])
for filename in filenames: #TODO: solve the problem of "KILLED" while wring file. Use 100 files temporarily before solving this.
produce_oracle(filename, feature_extractor)
# dump envTrainingData to a file for further learning in rewardNN.py
json_str = json.dumps(envTrainingData) + "\n"
json_bytes = json_str.encode('utf-8')
