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Does Theano meet your needs? Then no. Does TensorFlow meet them better, enough to justify the cost in switching? Then yes. "Actively developed" is a silly metric. Focus on features, flexibility, robustness etc.

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For neural network libraries this isn't sensible.

For many (most?) users outside of Google and Facebook the most important feature is "is there an off-the-shelf implementation of new technique XXX or do I have to build it myself?"

For most users the sensible choice comes down to Keras+Tensorflow or PyTorch.

Depends on what you do. If you're starting a new project picking Theano indeed isn't a very good choice due to the reasons you've mentioned. However, if you already have a stable piece of software that does what you want it to do then migration won't add much value and you could spend this time doing something more important, like improving documentation or having dinner with your family and friends.

However it's worth pointing out that theano's API is somewhat similar to tensorflow so migrating shouldn't be too hard and should be fairly easy to test

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