在keras中连接多个CNN模型

from keras.models import Model
from keras.layers import Input, concatenate
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.layers.core import Flatten, Dense

# 定义模型1
input1 = Input(shape=(32, 32, 3))
conv1_1 = Conv2D(32, (3, 3), activation='relu')(input1)
pool1_1 = MaxPooling2D(pool_size=(2, 2))(conv1_1)
conv1_2 = Conv2D(64, (3, 3), activation='relu')(pool1_1)
pool1_2 = MaxPooling2D(pool_size=(2, 2))(conv1_2)
flatten1 = Flatten()(pool1_2)
model1 = Model(inputs=input1, outputs=flatten1)

# 定义模型2
input2 = Input(shape=(64, 64, 3))
conv2_1 = Conv2D(64, (3, 3), activation='relu')(input2)
pool2_1 = MaxPooling2D(pool_size=(2, 2))(conv2_1)
conv2_2 = Conv2D(128, (3, 3), activation='relu')(pool2_1)
pool2_2 = MaxPooling2D(pool_size=(2, 2))(conv2_2)
flatten2 = Flatten()(pool2_2)
model2 = Model(inputs=input2, outputs=flatten2)

merged = concatenate([model1.output, model2.output])

dense1 = Dense(128, activation='relu')(merged)
output = Dense(10, activation='softmax')(dense1)

model = Model(inputs=[model1.input, model2.input], outputs=output)