The only static parts of the Tensor types are the Backend (Cpu, CUDA, ...) and the internal type (int32, float32, object ...).
The network topology will be dynamic and using dynamic graphs more akin to PyTorch/Chainer/DyNet than Theano/Tensorflow/Keras. My next step is to build an autograd so people only need to implement the forward pass, backpropagation will be automatic. For this part I'm waiting for VTable. PS: I think NimData is great too, Pandas seems like a much harder beast!
