tf.contrib.layers share a lot of functionality (standard 2D convolutional layers, batch normalization layers, etc). Is the difference between these two just that the
contrib.layers package is still experimental where the
layers package is considered stable? Or is one being replaced by the other? Other differences? Why are these two separate?
You’ve answered your own question. The description on the official documentation for the
tf.contrib namespace is:
contrib module containing volatile or experimental code.
tf.contrib is reserved for experimental features. APIs in this namespace are allowed to change rapidly between versions, whereas the others usually can’t without a new major version.
In particular, the functions in
tf.contrib.layers are not identical to those found in
tf.layers, although some of them might be replicated with different names.
As for whether you should use them, that depends on whether you are willing to handle sudden breaking changes. Code that doesn’t rely on
tf.contrib may be easier to migrate to future versions of TensorFlow.