def main(debug=True):
print 80 * "="
print "INITIALIZING"
print 80 * "="
config = Config()
parser, embeddings, train_examples, dev_set, test_set = load_and_preprocess_data(debug)
if not os.path.exists('./data/weights/'):
os.makedirs('./data/weights/')
with tf.Graph().as_default():
print "Building model...",
start = time.time()
model = ParserModel(config, embeddings)
parser.model = model
print "took {:.2f} seconds\n".format(time.time() - start)
init = tf.global_variables_initializer()
# If you are using an old version of TensorFlow, you may have to use
# this initializer instead.
# init = tf.initialize_all_variables()
saver = None if debug else tf.train.Saver()
with tf.Session() as session:
parser.session = session
session.run(init)
print 80 * "="
print "TRAINING"
print 80 * "="
model.fit(session, saver, parser, train_examples, dev_set)
if not debug:
print 80 * "="
print "TESTING"
print 80 * "="
print "Restoring the best model weights found on the dev set"
saver.restore(session, './data/weights/parser.weights')
print "Final evaluation on test set",
UAS, dependencies = parser.parse(test_set)
print "- test UAS: {:.2f}".format(UAS * 100.0)
print "Writing predictions"
with open('q2_test.predicted.pkl', 'w') as f:
cPickle.dump(dependencies, f, -1)
print "Done!"
dev_UAS, _ = parser.parse(dev_data)
print("- dev UAS: {:.2f}".format(dev_UAS * 100.0))
return dev_UAS
if __name__ == "__main__":
# Note: Set debug to False, when training on entire corpus
# debug = True
debug = False
assert(torch.__version__ == "1.0.0"), "Please install torch version 1.0.0"
print(80 * "=")
print(f"INITIALIZING debug={debug}")
print(80 * "=")
parser, embeddings, train_data, dev_data, test_data = load_and_preprocess_data(
debug)
start = time.time()
model = ParserModel(embeddings)
parser.model = model
print("took {:.2f} seconds\n".format(time.time() - start))
print(80 * "=")
print("TRAINING")
print(80 * "=")
output_dir = "results/{:%Y%m%d_%H%M%S}/".format(datetime.now())
output_path = output_dir + "model.weights"
if not os.path.exists(output_dir):
os.makedirs(output_dir)
def __init__(self, config, pretrained_embeddings):
self.pretrained_embeddings = pretrained_embeddings
self.config = config
self.build()
<<<<<<< HEAD
def main(debug=True):
=======
def main(debug=False):
>>>>>>> master
print(80 * "=")
print("INITIALIZING")
print(80 * "=")
config = Config()
parser, embeddings, train_examples, dev_set, test_set = load_and_preprocess_data(debug)
if not os.path.exists('./data/weights/'):
os.makedirs('./data/weights/')
with tf.Graph().as_default() as graph:
print("Building model...", end=' ')
start = time.time()
model = ParserModel(config, embeddings)
parser.model = model
init_op = tf.global_variables_initializer()
saver = None if debug else tf.train.Saver()
print("took {:.2f} seconds\n".format(time.time() - start))
graph.finalize()
with tf.Session(graph=graph).as_default() as session:
parser.session = session