我想集成ResNet50和DenseNet121，但遇到了一个错误：

图形断开连接：无法获取张量Tensor(“input_8:0”, shape=(?, 224, 224, 3), dtype=float32)在层“input_8”的值。之前访问的层没有问题：[]

下面是我集成的代码：

from keras import applicationsfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2Dfrom keras.models import Model, Input#from keras.engine.topology import Inputfrom keras.layers import Averagedef resnet50():    base_model = applications.resnet50.ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))    last = base_model.output    x = Flatten()(last)    x = Dense(2000, activation='relu')(x)    preds = Dense(200, activation='softmax')(x)    model = Model(base_model.input, preds)    return modeldef densenet121():    base_model = applications.densenet.DenseNet121(weights='imagenet', include_top=False, input_shape=(224,224, 3))    last = base_model.output    x = Flatten()(last)    x = Dense(2000, activation='relu')(x)    preds = Dense(200, activation='softmax')(x)    model = Model(base_model.input, preds)    return modelresnet50_model = resnet50()densenet121_model = densenet121()ensembled_models = [resnet50_model,densenet121_model]def ensemble(models,model_input):    outputs = [model.outputs[0] for model in models]    y = Average()(outputs)    model = Model(model_input,y,name='ensemble')    return modelmodel_input = Input(shape=(224,224,3))ensemble_model = ensemble(ensembled_models,model_input)

我认为原因是在我结合ResNet50和DenseNet121时，它们有自己的输入层，尽管我设定了相同的输入形状。不同的输入层导致了冲突。这只是我的猜测，我不确定如何解决这个问题

回答：

您可以在创建基础模型时设置input_tensor=model_input。

def resnet50(model_input):    base_model = applications.resnet50.ResNet50(weights='imagenet', include_top=False, input_tensor=model_input)    # ...def densenet121(model_input):    base_model = applications.densenet.DenseNet121(weights='imagenet', include_top=False, input_tensor=model_input)    # ...model_input = Input(shape=(224, 224, 3))resnet50_model = resnet50(model_input)densenet121_model = densenet121(model_input)

这样，基础模型将使用提供的model_input张量，而不是创建它们自己的独立输入张量。