Qfyilyi

I want to create a multi-input deep learning model. The model takes two inputs (images) from different datasets and calculates the average of them. See the code:

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)

I tried the following code to create the generator, but I got an error:

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)

I got this error:

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 ...

How can I solve this problem and create the generator?

The error is raised because your model has two inputs but in this line:

yield X1i[0], X2i[0]

The generator would return a tuple of two arrays. In fit_generator the first one would be interpreted as the model input and the second one would be interpreted as the model output. Hence you would get that error saying you have only passed one input to the model. To resolve this put the inputs in a list and also return the labels, whatever they should be:

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