tf-keras-skeleton.py 文件源码

def make_sampling_table(size, sampling_factor=1e-5):
          """Generates a word rank-based probabilistic sampling table.

          This generates an array where the ith element
          is the probability that a word of rank i would be sampled,
          according to the sampling distribution used in word2vec.

          The word2vec formula is:
              p(word) = min(1, sqrt(word.frequency/sampling_factor) /
              (word.frequency/sampling_factor))

          We assume that the word frequencies follow Zipf's law (s=1) to derive
          a numerical approximation of frequency(rank):
             frequency(rank) ~ 1/(rank * (log(rank) + gamma) + 1/2 - 1/(12*rank))
              where gamma is the Euler-Mascheroni constant.

          Arguments:
              size: int, number of possible words to sample.
              sampling_factor: the sampling factor in the word2vec formula.

          Returns:
              A 1D Numpy array of length `size` where the ith entry
              is the probability that a word of rank i should be sampled.
          """
          gamma = 0.577
          rank = np.array(list(range(size)))
          rank[0] = 1
          inv_fq = rank * (np.log(rank) + gamma) + 0.5 - 1. / (12. * rank)
          f = sampling_factor * inv_fq

          return np.minimum(1., f / np.sqrt(f))