clustering_ops.py 文件源码

def _init_clusters_random(self):
    """Does random initialization of clusters.

    Returns:
      Tensor of randomly initialized clusters.
    """
    num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in self._inputs])
    # Note that for mini-batch k-means, we should ensure that the batch size of
    # data used during initialization is sufficiently large to avoid duplicated
    # clusters.
    with ops.control_dependencies(
        [check_ops.assert_less_equal(self._num_clusters, num_data)]):
      indices = random_ops.random_uniform(
          array_ops.reshape(self._num_clusters, [-1]),
          minval=0,
          maxval=math_ops.cast(num_data, dtypes.int64),
          seed=self._random_seed,
          dtype=dtypes.int64)
      clusters_init = embedding_lookup(
          self._inputs, indices, partition_strategy='div')
      return clusters_init