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.device("/gpu:0"):
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", )
score, dependencies = parser.parse(test_set)
print("- test score: {:.2f}".format(score * 100.0))
print("Writing predictions")
with open('q2_test.predicted.pkl', 'wb') as f:
pickle.dump(dependencies, f, -1)
print("Done!")
def main(debug=True):
print(80 * "=")
print("INITIALIZING")
con = Config()
print((80 * "="), con)
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...")
t = time.time()
model = ParserModel(con, 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() - t))
graph.finalize()
with tf.Session(graph=graph) as session:
parser.session = session
session.run(init_op)
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:
pickle.dump(dependencies, f, -1)
print("Done!")
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/')
print ("Building model...",)
start = time.time()
model = ParserModel(config, embeddings)
parser.model = model
print ("took {:.2f} seconds\n".format(time.time() - start))
print( 80 * "=")
print( "TRAINING")
print( 80 * "=")
save = False if debug else True
model.fit(parser, save, train_examples, dev_set)
if not debug:
print (80 * "=")
print ("TESTING")
print (80 * "=")
print ("Restoring the best model weights found on the dev set")
model.W = t.load('./data/weights/W2.pth')
model.U = t.load('./data/weights/U2.pth')
model.embedded = t.load('./data/weights/embedded2.pth')
model.V = t.load('./data/weights/V2.pth')
model.b1 = t.load('./data/weights/b1_ext.pth')
model.b2 = t.load('./data/weights/b2_ext.pth')
model.b3 = t.load('./data/weights/b3_ext.pth')
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_extension.predicted.pkl', 'wb') as f:
pickle.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("INITIALIZING")
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)