What extra can be done using Keras functional API, which could not be done using keras sequential models? Apart from the fact that a simple model can be reused for a time bases data using “TimeDistributed” layer wrapper ?
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It is much more than model reuse, the functional API allows you to easily define models where layers connect to more than just the previous and next layers. You can connect layers to any other layers as you wish, so siamese networks, densely connected networks and such become possible. The old Graph API allowed the same level of connectivity but it was a PITA due to its use of layer node names to define connectivity.
The sequential model is just a sequential set of layers, and new neural network architectures at this time are moving away from such pattern.