Keras embedding layer masking. Why does input_dim need to be |vocabulary| + 2?

I believe the docs are a bit misleading there. In the normal case you are mapping your n input data indices [0, 1, 2, ..., n-1] to vectors, so your input_dim should be as many elements as you have

input_dim = len(vocabulary_indices)

An equivalent (but slightly confusing) way to say this, and the way the docs do, is to say

1 + maximum integer index occurring in the input data.

input_dim = max(vocabulary_indices) + 1

If you enable masking, value 0 is treated differently, so you increment your n indices by one: [0, 1, 2, ..., n-1, n], thus you need

input_dim = len(vocabulary_indices) + 1

or alternatively

input_dim = max(vocabulary_indices) + 2

The docs become especially confusing here as they say

(input_dim should equal |vocabulary| + 2)

where I would interpret |x| as the cardinality of a set (equivalent to len(x)), but the authors seem to mean

2 + maximum integer index occurring in the input data.

Because the input_dim already is +1 of the vocabulary, so you just add another +1 for the 0 and get the +2.

input_dim: int > 0. Size of the vocabulary, ie. 1 + maximum integer index occurring in the input data.