def METHOD_NAME():
    tf.compat.v1.set_random_seed(777)
    data_size = TOTAL_TRAIN_DATA_SIZE;
    InputVector = np.zeros((TOTAL_LABEL_SIZE*data_size, FEATURE_SIZE),dtype=np.float32)
    InputLabel = np.zeros((TOTAL_LABEL_SIZE*data_size, TOTAL_LABEL_SIZE),dtype=np.float32)
    ValVector = np.zeros((TOTAL_LABEL_SIZE*TOTAL_VAL_DATA_SIZE,FEATURE_SIZE),dtype=np.float32)
    ValLabel = np.zeros((TOTAL_LABEL_SIZE*TOTAL_VAL_DATA_SIZE, TOTAL_LABEL_SIZE),dtype=np.float32)
    #read Input & Label
    fin = open('valSet.dat','rb')
    for i in range(TOTAL_LABEL_SIZE*TOTAL_VAL_DATA_SIZE):
        for j in range(FEATURE_SIZE):
            data_str = fin.read(4)
            ValVector[i,j] = struct.unpack('f',data_str)[0]
        for j in range(TOTAL_LABEL_SIZE):
            data_str = fin.read(4)
            ValLabel[i,j] = struct.unpack('f',data_str)[0]
    fin.close()
    fin=open('trainingSet.dat','rb')
    for i in range(TOTAL_LABEL_SIZE*data_size):
        for j in range(FEATURE_SIZE):
            data_str = fin.read(4)
            InputVector[i,j] = struct.unpack('f',data_str)[0]
        for j in range(TOTAL_LABEL_SIZE):
            data_str = fin.read(4)
            InputLabel[i,j] = struct.unpack('f',data_str)[0]
    fin.close()
    return InputVector, InputLabel, ValVector, ValLabel