Ah, fair enough, I see your point. An imperative approach (versus TensorFlow's semi-declarative approach) can be easier to specialize to dynamic compute graphs.

I personally think the approach used in TensorFlow is preferable – having a static graph enables a lot of convenient operations, such as storing a fixed graph data structure, shipping models that are independent of code, performing graph transformations. But you're right that it entails a bit more complexity, and that implementing something like recursive neural networks, while totally possible in a neat way, ends up taking a bit more effort. I think that the trade-off is worth it in the long run, and that the design of TensorFlow is very much influenced by the long-run view (at the expense of immediate simplicity...).

The ops underlying TensorFlow's `tf.while_loop` are actually quite flexible, so I imagine you can create a lot of different looping constructs with them, including ones that easily handle recursive neural networks.

Thanks for pointing out a problem that I haven't really thought about before!