advanced deep-learning best practices features from the left camera and the right camera before merging the two feeds such low-level processing can be shared across the two inputsthat isdone via layers that use the same weights and thus share the same representations here' how you' implement siamese vision model (shared convolutional basein kerasfrom keras import layers from keras import applications from keras import input the base image-processing model is the xception network (convolutional base onlyxception_base applications xception(weights=noneinclude_top=falseleft_input input(shape=( )right_input input(shape=( )the inputs are rgb images left_features xception_base(left_inputright_input xception_base(right_inputcalls the same vision model twice merged_features layers concatenate[left_featuresright_input]axis=- the merged features contain information from the right visual feed and the left visual feed wrapping up this concludes our introduction to the keras functional api--an essential tool for building advanced deep neural network architectures now you know the followingto step out of the sequential api whenever you need anything more than linear stack of layers how to build keras models with several inputsseveral outputsand complex internal network topologyusing the keras functional api how to reuse the weights of layer or model across different processing branchesby calling the same layer or model instance several times licensed to