categorical.py 文件源码

def _log_prob(self, k):
    k = ops.convert_to_tensor(k, name="k")
    if self.logits.get_shape()[:-1] == k.get_shape():
      logits = self.logits
    else:
      logits = self.logits * array_ops.ones_like(
          array_ops.expand_dims(k, -1), dtype=self.logits.dtype)
      logits_shape = array_ops.shape(logits)[:-1]
      k *= array_ops.ones(logits_shape, dtype=k.dtype)
      k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
    return -nn_ops.sparse_softmax_cross_entropy_with_logits(labels=k,
                                                            logits=logits)