我想创建一个多输入的深度学习模型。该模型接受来自不同数据集的两个输入（图像）并计算它们的平均值。请看代码：

input1 = keras.layers.Input(shape=(16,))x1 = keras.layers.Dense(8, activation='relu')(input1)input2 = keras.layers.Input(shape=(32,))x2 = keras.layers.Dense(8, activation='relu')(input2)a = keras.layers.average([x1, x2])out = keras.layers.Dense(4)(a)model = keras.models.Model(inputs=[input1, input2], outputs=out)

我尝试使用以下代码创建生成器，但遇到了错误：

input_imgen = ImageDataGenerator(                                   rotation_range=10,                                  shear_range=0.2,                                  zoom_range=0.1,                                  width_shift_range=0.1,                                  height_shift_range=0.1                                  )test_imgen = ImageDataGenerator()def generate_generator_multiple(generator,dir1, dir2, batch_size, img_height,img_width):    genX1 = generator.flow_from_directory(dir1,                                          target_size = (img_height,img_width),                                          class_mode = 'categorical',                                          batch_size = batch_size,                                          shuffle=False,                                           seed=7)    genX2 = generator.flow_from_directory(dir2,                                          target_size = (img_height,img_width),                                          class_mode = 'categorical',                                          batch_size = batch_size,                                          shuffle=False,                                           seed=7)    while True:        X2i = genX2.next()            X1i = genX1.next()        yield X1i[0], X2i[0] inputgenerator=generate_generator_multiple(generator=input_imgen,                                           dir1=train_data1,                                           dir2=train_data2,                                           batch_size=32,                                           img_height=224,                                           img_width=224)       validgenerator=generate_generator_multiple(generator=test_imgen,                                          dir1=valid_data1,                                          dir2=valid_data2,                                          batch_size=32,                                          img_height=224,                                          img_width=224) testgenerator=generate_generator_multiple(generator=test_imgen,                                          dir1=test_data1,                                          dir2=test_data2,                                          batch_size=32,                                          img_height=224,                                          img_width=224)    # compile the model    multi_model.compile(            loss='categorical_crossentropy',            optimizer=Adam(lr=0.0001),            metrics=['accuracy']        )# train the model and save the historyhistory = multi_model.fit_generator(inputgenerator,steps_per_epoch=len(train_data) // batch_size,epochs=10,verbose=1,validation_data=validgenerator,validation_steps=len(valid_data) // batch_size,use_multiprocessing=True,shuffle=False)

我遇到了这个错误：

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [array([[[[108.930984, 108.930984, 108.930984],         [113.63957 , 113.63957 , 113.63957 ],         [113.07516 , 113.07516 , 113.07516 ],         ...,         [ 99.46968 ,  99.46968 ,  99.46968 ...

如何解决这个问题并创建生成器？

回答：

错误产生的原因是您的模型有两个输入，但在这一行代码中：

yield X1i[0], X2i[0]

生成器会返回一个包含两个数组的元组。在fit_generator中，第一个数组会被解释为模型的输入，第二个数组会被解释为模型的输出。因此，您会得到一个错误，指出您只传递了一个输入给模型。为了解决这个问题，将输入放在一个列表中，并且还要返回标签，无论它们是什么：

yield [X1i[0], X2i[0]], the_labels_array