我想用相同的输入连接两个迁移学习模型，两个模型并行运行，然后将合并的特征扁平化用于图像分类。但是我不知道为什么我得到了这个错误。谢谢！

input = tf.keras.layers.Input(shape=(300,300,3))
from tensorflow.keras.applications import ResNet50V2
base_model2 = ResNet50V2(weights='imagenet', include_top=False, input_tensor=input)
for layers in (base_model2.layers)[:90]:
  layers.trainable = False
from tensorflow.keras.applications import InceptionResNetV2
base_model1 = InceptionResNetV2(weights='imagenet', include_top=False, input_tensor=input)
for layers in (base_model1.layers)[:90]:
  layers.trainable = False
output = Concatenate()([base_model1, base_model2] , axis= 1)
output = Flatten()(output)
output = Dropout(0.8)(output)
output = Dense(1, activation='sigmoid')(output)
combine = Model(input = input, output = output)

错误消息：

我尝试将两个迁移学习模型串联起来，这样我将有一个模型，输入图像，并有两个迁移学习模型进行特征提取，并并行运行它，进行图像分类