def init_test():
mode_train, mode_test = 'TR', 'TE'
dict_obj = set_dict.Dictionary()
# train object
params_train = set_params.ParamsClass(mode=mode_train)
dir_train = set_dir.Directory(mode_train)
params_train.num_classes = len(dict_obj.label_dict)
# test object
params_test = set_params.ParamsClass(mode=mode_test)
dir_test = set_dir.Directory(mode_test)
params_test.num_instances, params_test.indices = get_length(dir_test.data_filename)
params_test.batch_size = 1
params_test.num_classes = len(dict_obj.label_dict)
word_emb_path = dir_train.word_embedding
word_emb_matrix = np.float32(np.genfromtxt(word_emb_path, delimiter=' '))
params_train.vocab_size = params_test.vocab_size = len(word_emb_matrix)
print('***** INITIALIZING TF GRAPH *****')
session = tf.Session()
with tf.variable_scope("classifier", reuse=None):
test_obj = model.DeepAttentionClassifier(params_test, dir_test)
model_saver = tf.train.Saver()
print('Loading model ...')
model_saver.restore(session, set_dir.Directory('TE').test_model)
print('**** MODEL LOADED ****\n')
return session, test_obj
def main():
session, test_obj = init_test()
dict_obj = set_dict.Dictionary()
run_test(session, test_obj, dict_obj)
def load_dictionary():
"""
Utility function to load training vocab files
:return:
"""
return set_dict.Dictionary('TE')