# What does updated alpha mean in LDA model?

I'm trying to understand LDA model by reading through implementations of the algorithm.

Many implementations update alpha during training iterations with codes like:

psi_sum1 = 0.0e0;
psi_sum2 = 0.0e0;

for (int d = 0; d < doc_size; d++)
{
psi_sum1 += digamma(float(n_dk[d][k]) + alpha_old[k]) - digamma(alpha[k]);
psi_sum2 += digamma(float(doc_words[d]) + alpha_sum) - digamma(alpha_sum);
}

alpha[k] = alpha_old[k] * psi_sum1 / psi_sum2;


e.g. for 10 topics, the init alpha could be [1, 1, ..., 1] (length = 10), after training, the alpha is updated to [0.29, 0.30, ..., 0.28] (length = 10).

What does this updated alpha mean?

Can I use the updated alpha to determine whether the topic number is appropriate?