It's worth noting than BNNS (the CPU library Apple released) defines just 3 layer types. It's clear they are for now targeting just convolutional networks (the kind you'd use for computer vision), and even then just the simplest possible kind (for example Alexnet, now ~4 years old).

There needs to be much more functionality to build a state-of-the-art vision network, such as Google's Inception v3, at least if you want to avoid writing your own layer definitions. Hopefully they'll expand this out later. In the foundation keynote they did mention using LSTMs for predictive text, so hopefully more advanced things are in the pipeline and just haven't been API-ified yet.

For reference, the deep learning library I'm working on will contain 17 layer types in its first version, with later versions probably asymptoting to double that. There are a LOT of things out there that deserve their own layer types.

EDIT: to be fair to Apple, since convolution, pooling, and fully connected layers are among the most costly layers, they are the most valuable to have efficient platform implementations for.