在keras中连接四个CNN模型

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

# 模型1
input1 = Input(shape=(64, 64, 3))
conv1_1 = Conv2D(32, (3, 3), activation='relu')(input1)
pool1_1 = MaxPooling2D(pool_size=(2, 2))(conv1_1)

# 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)

# 模型3
input3 = Input(shape=(64, 64, 3))
conv3_1 = Conv2D(128, (3, 3), activation='relu')(input3)
pool3_1 = MaxPooling2D(pool_size=(2, 2))(conv3_1)

# 模型4
input4 = Input(shape=(64, 64, 3))
conv4_1 = Conv2D(256, (3, 3), activation='relu')(input4)
pool4_1 = MaxPooling2D(pool_size=(2, 2))(conv4_1)

concat = concatenate([pool1_1, pool2_1, pool3_1, pool4_1])

flatten = Flatten()(concat)
dense1 = Dense(128, activation='relu')(flatten)
output = Dense(10, activation='softmax')(dense1)

model = Model(inputs=[input1, input2, input3, input4], outputs=output)