注意传入的是logits

@tf_export("nn.weighted_cross_entropy_with_logits")
def weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None):
  """Computes a weighted cross entropy.

  This is like `sigmoid_cross_entropy_with_logits()` except that `pos_weight`,
  allows one to trade off recall and precision by up- or down-weighting the
  cost of a positive error relative to a negative error.

  The usual cross-entropy cost is defined as:

      targets * -log(sigmoid(logits)) +
          (1 - targets) * -log(1 - sigmoid(logits))

  A value `pos_weights > 1` decreases the false negative count, hence increasing
  the recall.
  Conversely setting `pos_weights < 1` decreases the false positive count and
  increases the precision.
  This can be seen from the fact that `pos_weight` is introduced as a
  multiplicative coefficient for the positive targets term
  in the loss expression:

      targets * -log(sigmoid(logits)) * pos_weight +
          (1 - targets) * -log(1 - sigmoid(logits))

  For brevity, let `x = logits`, `z = targets`, `q = pos_weight`.
  The loss is:

        qz * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
      = qz * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
      = qz * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
      = qz * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
      = (1 - z) * x + (qz +  1 - z) * log(1 + exp(-x))
      = (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))

  Setting `l = (1 + (q - 1) * z)`, to ensure stability and avoid overflow,
  the implementation uses

      (1 - z) * x + l * (log(1 + exp(-abs(x))) + max(-x, 0))

  `logits` and `targets` must have the same type and shape.

  Args:
    targets: A `Tensor` of the same type and shape as `logits`.
    logits: A `Tensor` of type `float32` or `float64`.
    pos_weight: A coefficient to use on the positive examples.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of the same shape as `logits` with the componentwise
    weighted logistic losses.

  Raises:
    ValueError: If `logits` and `targets` do not have the same shape.
  """
  with ops.name_scope(name, "logistic_loss", [logits, targets]) as name:
    logits = ops.convert_to_tensor(logits, name="logits")
    targets = ops.convert_to_tensor(targets, name="targets")
    try:
      targets.get_shape().merge_with(logits.get_shape())
    except ValueError:
      raise ValueError(
          "logits and targets must have the same shape (%s vs %s)" %
          (logits.get_shape(), targets.get_shape()))

    # The logistic loss formula from above is
    #   (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(-x))
    # For x < 0, a more numerically stable formula is
    #   (1 - z) * x + (1 + (q - 1) * z) * log(1 + exp(x)) - l * x
    # To avoid branching, we use the combined version
    #   (1 - z) * x + l * (log(1 + exp(-abs(x))) + max(-x, 0))
    log_weight = 1 + (pos_weight - 1) * targets
    return math_ops.add(
        (1 - targets) * logits,
        log_weight * (math_ops.log1p(math_ops.exp(-math_ops.abs(logits))) +
                      nn_ops.relu(-logits)),
        name=name)