Question

我想创建一个多输入深度学习模型。该模型从不同的数据集中获取两个输入（图像），并计算它们的平均值。查看代码：

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，但出现错误：

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 history
history = 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)

我收到此错误：

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 ...

如何解决此问题并创建生成器？

Answer 1

引发错误，因为您的模型有两个输入，但在此行中：

yield X1i[0], X2i[0]

生成器将返回两个数组的元组。在fit_generator中，第一个解释为模型输入，第二个解释为模型输出。因此，您将得到该错误，提示您仅将一个输入传递给模型。要解决此问题，请将输入放入列表中，并返回标签，无论它们是什么：

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

多输入深度学习模型中两个输入的平均值

1 个答案: