A consequence of this decision is that Keras has its own graph datastructure for handling computational graphs, rather than relying on the native graph datastructure from TensorFlow or Theano. As a result, Keras can do offline shape inference in Theano (a much needed yet missing feature in Theano), and can do easy model sharing or model copying. For instance, when you call a Keras model on a new input ( y model(x)), Keras is reapplying all operations contained in the graph underlying your model, which is made possible by the fact that Keras manages that graph independently of TensorFlow/Theano. In fact, it's even possible to: 1) define a Keras model with the Theano backend, 2) switch to the TensorFlow backend, and 3) re-apply your (Theano-built) Keras model on a TensorFlow input, this creating a TF version of what was initially a Theano model (note that in practice we don't allow to switch backends in the middle of a session, since that would be quite unsafe --the user may mix up TF and Theano tensors-- but it's possible to do it manually if you are familiar with Keras internals).