keras实现基于孪生网络的图片相似度计算方式

import keras
from keras.layers import Input,Dense,Conv2D
from keras.layers import MaxPooling2D,Flatten,Convolution2D
from keras.models import Model
import os
import numpy as np
from PIL import Image
from keras.optimizers import SGD
from scipy import misc
root_path = os.getcwd()
train_names = ['bear','blackswan','bus','camel','car','cows','dance','dog','hike','hoc','kite','lucia','mallerd','pigs','soapbox','stro','surf','swing','train','walking']
test_names = ['boat','dance-jump','drift-turn','elephant','libby']
 
def load_data(seq_names,data_number,seq_len): 
#生成图片对
  print('loading data.....')
  frame_num = 51
  train_data1 = []
  train_data2 = []
  train_lab = []
  count = 0
  while count < data_number:
    count = count + 1
    pos_neg = np.random.randint(0,2)
    if pos_neg==0: