es_file = sys.argv[3] + "/es_" + sys.argv[2] + ".txt"
es_epoch = sys.maxsize
if os.path.isfile(es_file) == True:
with open(es_file, 'r') as myfile:
es_epoch = int(myfile.read())
myfile.close()
return es_epoch
if __name__ == "__main__":
es_epoch = checkInputs()
config = build_data(sys.argv[1])
config.train_id_docs.extend(config.dev_id_docs)
train_data = utils.HeadData(config.train_id_docs, np.arange(len(config.train_id_docs)))
test_data = utils.HeadData(config.test_id_docs, np.arange(len(config.test_id_docs)))
tf.reset_default_graph()
tf.set_random_seed(1)
utils.printParameters(config)
# ---- Training ----
config1 = tf.ConfigProto()
config1.gpu_options.per_process_gpu_memory_fraction = 0.85
with tf.Session(config=config1) as sess:
# saver = tf.train.import_meta_graph('model.ckpt.meta')
# saver.restore(sess, 'model.ckpt')
embedding_matrix = tf.get_variable('embedding_matrix', shape=config.wordvectors.shape, dtype=tf.float32,
trainable=False).assign(config.wordvectors)
emb_mtx = sess.run(embedding_matrix)
model = tf_utils.model(config, emb_mtx, sess)
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
gpuConfig = tf.ConfigProto(allow_soft_placement=True)
gpuConfig.gpu_options.allow_growth = True
if __name__ == "__main__":
# checkInputs()
config = build_data("./configs/CoNLL04/bio_config")
train_data = utils.HeadData(config.train_id_docs,
np.arange(len(
config.train_id_docs))) ## build data
dev_data = utils.HeadData(config.dev_id_docs,
np.arange(len(config.dev_id_docs)))
test_data = utils.HeadData(config.test_id_docs,
np.arange(len(config.test_id_docs)))
tf.reset_default_graph()
tf.set_random_seed(1)
utils.printParameters(config)
with tf.Session(config=gpuConfig) as sess:
embedding_matrix = tf.get_variable('embedding_matrix',
shape=config.wordvectors.shape,
dtype=tf.float32,