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I Want to Combine Two CNN Into Just One In Keras, What I Mean Is that I Want The Neural Network To Take Two Images And Process Each One in Separate CNN, and Then Concatenate Them Together Into The Flattening Layer and Use Fully Connected Layer to Do The Last Work, Here What I Did:

# Start With First Branch ############################################################

branch_one = Sequential()



# Adding The Convolution

branch_one.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'))

branch_one.add(Conv2D(32, (3, 3), activation='relu'))



# Doing The Pooling Phase

branch_one.add(MaxPooling2D(pool_size=(2, 2)))

branch_one.add(Dropout(0.25))

branch_one.add(Flatten())



# Start With Second Branch ############################################################



branch_two = Sequential()



# Adding The Convolution

branch_two.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'))

branch_two.add(Conv2D(32, (3, 3), activation='relu'))



# Doing The Pooling Phase

branch_two.add(MaxPooling2D(pool_size=(2, 2)))

branch_two.add(Dropout(0.25))

branch_two.add(Flatten())



# Making The Combinition ##########################################################

final = Sequential()

final.add(Concatenate([branch_one, branch_two]))

final.add(Dense(units = 128, activation = "relu"))

final.add(Dense(units = 1, activation = "sigmoid"))



# Doing The Compilation

final.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

# Adding and Pushing The Images to CNN



# use ImageDataGenerator to preprocess the data



from keras.preprocessing.image import ImageDataGenerator



# augment the data that we have

train_datagen = ImageDataGenerator(rescale = 1./255,

                                   shear_range = 0.2,

                                   zoom_range = 0.2,

                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)



# prepare training data

X1 = train_datagen.flow_from_directory('./ddsm1000_resized/images/train',

                                                 target_size = (64, 64),

                                                 batch_size = 32,

                                                 class_mode = 'binary')



X2 = train_datagen.flow_from_directory('./ddsm1000_resized_canny/images/train',



                                                 target_size = (64, 64),

                                                 batch_size = 32,

                                                 class_mode = 'binary')



# prepare test data

Y1 = test_datagen.flow_from_directory('./ddsm1000_resized/images/test',

                                            target_size = (64, 64),

                                            batch_size = 32,

                                            class_mode = 'binary')

Y2 = test_datagen.flow_from_directory('./ddsm1000_resized_canny/images/test',

                                            target_size = (64, 64),

                                            batch_size = 32,

                                            class_mode = 'binary')

final.fit_generator([X1, X2], steps_per_epoch = (8000 / 32), epochs = 1, validation_data = [Y1,Y2], validation_steps = 2000)

Keras Telling Me

RuntimeError: You must compile your model before using it.

I Think That is The CNN Does not the shapes of input data, so what Can I Do Here ?? Thanks