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
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')
with gzip.GzipFile('env-{}.json'.format(sys.argv[1]), 'w') as fout:
