def do_evaluate(args):
config = Config(args.model_path)
helper = ModelHelper.load(args.model_path)
input_data = read_conll(args.data)
embeddings = load_embeddings(args, helper)
config.embed_size = embeddings.shape[1]
logger.info("Building model...", )
start = time.time()
model = WindowModel(helper, config, embeddings)
logger.info("took %.2f seconds", time.time() - start)
for sentence, labels, predictions in model.output(input_data):
predictions = [LBLS[l] for l in predictions]
print_sentence(args.output, sentence, labels, predictions)
def do_evaluate(args):
config = Config(args.model_path)
helper = ModelHelper.load(args.model_path)
input_data = read_conll(args.data)
embeddings = load_embeddings(args, helper)
config.embed_size = embeddings.shape[1]
with tf.Graph().as_default():
logger.info("Building model...",)
start = time.time()
model = WindowModel(helper, config, embeddings)
logger.info("took %.2f seconds", time.time() - start)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as session:
session.run(init)
saver.restore(session, model.config.model_output)
for sentence, labels, predictions in model.output(session, input_data):
predictions = [LBLS[l] for l in predictions]
print_sentence(args.output, sentence, labels, predictions)
def do_evaluate(args):
config = Config(args.model_path)
helper = ModelHelper.load(args.model_path)
input_data = read_conll(args.data)
embeddings = load_embeddings(args, helper)
config.embed_size = embeddings.shape[1]
with tf.Graph().as_default():
logger.info("Building model...", )
start = time.time()
model = WindowModel(helper, config, embeddings)
logger.info("took %.2f seconds", time.time() - start)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as session:
session.run(init)
saver.restore(session, model.config.model_output)
for sentence, labels, predictions in model.output(session, input_data):
predictions = [LBLS[l] for l in predictions]
print_sentence(args.output, sentence, labels, predictions)