I'm trying to implement a custom layer in Keras where I need to convert a tensor of floats [a, 1+a) to a binary tensor for masking. I can see that Tensorflow has a floor function that can do that, but Keras doesn't seem to have it in keras.backend. Any idea how I can do this?
As requested by OP, I will mention the answer I gave in my comment and elaborate more:
Short answer: you won't encounter any major problems if you use tf.floor().
Long answer: Using Keras backend functions (i.e. keras.backend.*) is necessary in those cases when 1) there is a need to pre-process or augment the argument(s) passed to actual function of Tensorflow or Theano backend or post-process the returned results. For example, the mean method in backend can also work with boolean tensors as input, however the reduce_mean method in TF expects numerical types as input; or 2) you want to write a model that works across all the Keras supported backends.
Otherwise, it is fine to use most of real backend functions directly; however, if the function has been defined in keras.backend module, then it is recommended to use that instead.