### `tf.nn.weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None)` {#weighted_cross_entropy_with_logits}

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))

The argument `pos_weight` is used as a multiplier for the positive targets:

  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:


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

##### Returns:

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

##### Raises:


*  <b>`ValueError`</b>: If `logits` and `targets` do not have the same shape.

