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tf.nn.weighted_cross_entropy_with_logits

TensorFlow 2.0 version View source on GitHub

Computes a weighted cross entropy. (deprecated arguments)

Aliases:

  • tf.compat.v1.nn.weighted_cross_entropy_with_logits
tf.nn.weighted_cross_entropy_with_logits(
    labels=None,
    logits=None,
    pos_weight=None,
    name=None,
    targets=None
)

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: labels * -log(sigmoid(logits)) + (1 - labels) * -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 labels term in the loss expression: labels * -log(sigmoid(logits)) * pos_weight + (1 - labels) * -log(1 - sigmoid(logits)) For brevity, let x = logits, z = labels, 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 labels must have the same type and shape. Args: labels: 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). targets: Deprecated alias for labels.

Returns:

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

Raises:

  • ValueError: If logits and labels do not have the same shape.