我正在尝试训练一个AI来识别基于图像和患者信息的病变。我使用Keras的Sequential模型来实现这一目标。我创建了两个顺序模型，然后将它们合并并编译合并后的模型。当我尝试拟合模型时，我得到了错误RuntimeError: You must compile your model before using it.，尽管我的模型已经定义了输入形状。
我尝试将input_dim=dim改为input_shape=(dim,)。我找到的关于此问题的唯一信息，例如这个帖子或这个帖子，都只是说要确保你合并的模型的第一层有一个定义的input_shape，而我的模型已经有了。我无法想象你还需要为Concatenate层做同样的事情。
我首先为患者信息创建密集层：
metadata_model = Sequential()metadata_model.add(Dense(32, input_dim=X_train.iloc[:, L*W:].shape[1], activation="relu"))metadata_model.add(Dense(64))然后是图像的模型：
model = Sequential()model.add(Conv2D(32, (3, 3), padding="same", input_shape=(W, L, 3)))model.add(Activation("relu"))model.add(BatchNormalization(axis=-1))model.add(MaxPooling2D(pool_size=(3,3)))model.add(Dropout(rate = 0.25))model.add(Conv2D(64, (3, 3), padding="same"))model.add(Activation("relu"))model.add(BatchNormalization(axis=-1))model.add(Conv2D(64, (3, 3), padding="same"))model.add(Activation("relu"))model.add(BatchNormalization(axis=-1))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(128, (3, 3), padding="same"))model.add(Activation("relu"))model.add(BatchNormalization(axis=-1))model.add(Conv2D(128, (3, 3), padding="same"))model.add(Activation("relu"))model.add(BatchNormalization(axis=-1))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(1024))model.add(Activation("relu"))model.add(BatchNormalization())model.add(Dropout(0.5))然后我将它们合并：
merged_model = Sequential()merged_model.add(Concatenate([model, metadata_model]))merged_model.add(Dense(7)) #7个病变类别merged_model.add(Activation("softmax"))编译并创建一个ImageDataGenerator：
opt = Adam(lr=INIT_LR, decay=INIT_LR/EPOCHS)merged_model.compile(loss="categorical_crossentropy", optimizer = opt, metrics=["accuracy"])aug = ImageDataGenerator(rotation_range=25, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode="nearest")并尝试训练它：
train = merged_model.fit_generator(aug.flow([trainInput, X_train.iloc[:, L*W:]], labels, batch_size=BS),validation_data=([testInput, X_test.iloc[:, L*W:]], labels_test),steps_per_epoch=500,epochs=EPOCHS,verbose=1)这一行导致以下错误：
RuntimeError Traceback (most recent call last)<ipython-input-114-fc6c254db390> in <module> 4 steps_per_epoch=500, 5 epochs=EPOCHS,----> 6 verbose=1)c:\users\megag\appdata\local\programs\python\python37\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs) 89 warnings.warn('Update your `' + object_name + '` call to the ' + 90 'Keras 2 API: ' + signature, stacklevel=2)---> 91 return func(*args, **kwargs) 92 wrapper._original_function = func 93 return wrapperc:\users\megag\appdata\local\programs\python\python37\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch) 1416 use_multiprocessing=use_multiprocessing, 1417 shuffle=shuffle,-> 1418 initial_epoch=initial_epoch) 1419 1420 @interfaces.legacy_generator_methods_supportc:\users\megag\appdata\local\programs\python\python37\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch) 38 39 do_validation = bool(validation_data)---> 40 model._make_train_function() 41 if do_validation: 42 model._make_test_function()c:\users\megag\appdata\local\programs\python\python37\lib\site-packages\keras\engine\training.py in _make_train_function(self) 494 def _make_train_function(self): 495 if not hasattr(self, 'train_function'):--> 496 raise RuntimeError('You must compile your model before using it.') 497 self._check_trainable_weights_consistency() 498 if self.train_function is None:RuntimeError: You must compile your model before using it.回答：
你的合并模型不再是顺序的（因为它有两个输入层/分支），因此你不能使用Sequential API。相反，你需要使用Keras的函数式API来合并你的模型：
from keras.models import Modelx = Concatenate()([model.output, metadata_model.output])x = Dense(7)(x)out = Activation("softmax")(x)merged_model = Model([model.input, metadata_model.input], out)# 其余部分相同...