The term 'size' isn't applicable to the tensor output of a network or network. These are the qualities.
- Rank $N$ that defines the rank of each tensor instance in $\mathbb{R}^N$
- Ranges of the indices to the dimensions from $1$ to $N$
- Ranges of the scalar values that comprise the tensor instance — If they are discrete rather than real (approximated by floating point or fixed point numbers), then the range is the description of the permissible ordinal values.
The question may be referring to this last quality.
The imposition of a penalty for values that are in the range of the numeric type used as the output of the last activation function but not in the allowable range of output for the desired trained function works in a limited way. It skews the output distribution with respect to the natural distribution of possible learning states and therefore can easily interfere with convergence quality or speed or both.
There are a number of techniques that map natural output distribution with constrained ranges, but it must be done without skewing the distribution upstream from the technique used, to avoid negatively impacting favorable convergence properties of the artificial network.
One simple case that can be described here is when the number of possible output states is in the set of $2^i$ where $i \in$ { positive integers }. In such a case, the final layer of the network can be $i$ threshold activation functions with 1 or -1 as possible output values.
In that case, the ordinal then becomes
$$o = \sum_{n=0}^i \frac {y_n + 1} {2},$$
where $y_n$ is the output from activation function index $n$.