def do_evaluate(args):
config = Config(args.model_path)
helper = ModelHelper.load(args.model_path)
embeddings = load_glove_vectors("../data/glove.6B.50d.txt", helper)
config.embed_size = embeddings.shape[1]
with tf.Graph().as_default():
logger.info("Building model...", )
start = time.time()
model = NGramModel(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 i in range(1, 8):
input_data = read_book_ngrams('../data/' + str(i) +
'_5grams.txt')
result = model.predict_book_age(session, input_data[0],
input_data[1])
print LBLS[result], input_data[1]
def evaluate(args, config):
test_dir = args.test
datautil = DataUtil(config)
helper = ModelHelper.load(args.model_path)
test_raw = read_conll(test_dir)
test_data = helper.vectorize(test_raw, config)
embeddings = datautil.load_embeddings(args.embedding, helper)
with tf.Graph().as_default():
logger.info("Building model...", )
start = time.time()
model = None
if config.model == 'tcn':
model = TemporalConvnet(helper, config, embeddings, datautil)
elif config.model == 'bilstm':
model = BiLSTM(helper, config, embeddings, datautil)
else:
logger.warning("Selected model does not exist!")
assert False
assert model is not None, "Model is None, stop excuted!"
logger.info("took %.2f seconds", time.time() - start)
test_examples = model.preprocess_sequence_data(test_data)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
saver.restore(sess, config.model_output)
token_cm, entity_scores = model.evaluate(sess, test_examples,
test_data)
logger.debug("Token-level confusion matrix:\n" +
token_cm.as_table())
logger.info("Entity level P/R/F1: %.4f/%.4f/%.4f", *entity_scores)
def do_evaluate(args):
config = Config(args.model_path)
helper = ModelHelper.load(args.model_path)
input_data = read_ngrams(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 = NGramModel(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_shell(args):
config = Config(args)
helper = ModelHelper.load(args.model_path)
embeddings = load_embeddings(args, helper)
config.embed_size = embeddings.shape[1]
with tf.Graph().as_default():
logger.info("Building model...", )
start = time.time()
model = RNNModel(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)
print("""Welcome!
You can use this shell to explore the behavior of your model.
Please enter sentences with spaces between tokens, e.g.,
input> Germany 's representative to the European Union 's veterinary committee .
""")
while True:
# Create simple REPL
try:
sentence = raw_input("input> ")
tokens = sentence.strip().split(" ")
for sentence, _, predictions in model.output(
session, [(tokens, ["O"] * len(tokens))]):
predictions = [LBLS[l] for l in predictions]
print_sentence(sys.stdout, sentence,
[""] * len(tokens), predictions)
except EOFError:
print("Closing session.")
break