from lib.model.configs import cfg
from data_test.ant.util import get_model_list
# Parameters
# ==================================================
emb = Embedding(cfg)
if cfg.test_data is None:
print("test_data is empty.")
exit()
test_data = pd.read_csv(cfg.test_data, sep='\t')
x_test, y_test = emb.generate_sentence_token_ind(test_data)
x1_test, x2_test = zip(*x_test)
model_list = get_model_list(cfg.model_directory)
# print checkpoint_file
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False)
sess = tf.Session(config=session_conf)
with sess.as_default(), tf.device("/gpu:%s" % GPU):
report_list = []
for model in model_list:
checkpoint_file = model
# Load the saved meta graph and restore variables
saver = tf.train.import_meta_graph(
"{}.meta".format(checkpoint_file))
print("test_data is empty.")
exit()
emb = Embedding(cfg)
train_data = pd.read_csv(cfg.train_data, sep='\t')
x_train, y_train = emb.generate_sentence_token_ind(train_data)
test_data = pd.read_csv(cfg.test_data, sep='\t')
x_test, y_test = emb.generate_sentence_token_ind(test_data)
disan_model_path = os.path.join(root_path, 'model/disan_models')
bimpm_model_path = os.path.join(root_path, 'model/bimpm_models')
bimpm_pinyin_model_path = os.path.join(root_path, 'model/bimpm_pinyin_models')
disan_pingying_model_path = os.path.join(root_path, 'model/disan_pinyin_models')
disan_models = get_model_list(disan_model_path)[1:2]
bimpm_models = get_model_list(bimpm_model_path)[1:3]
bimpm_pinyin_models = get_model_list(bimpm_pinyin_model_path)
disan_pingying_models = get_model_list(disan_pingying_model_path)
models_count = len(disan_models) + len(bimpm_models)\
+ len(bimpm_pinyin_models) + len(disan_pingying_models)
# print checkpoint_file
all_train_scores = []
all_test_scores = []
cfg.dropout = 1
cfg.dropout_rate = 0.0
graph = tf.Graph()
with graph.as_default():