tf.losses.huber_loss

View source on GitHub

Adds a Huber Loss term to the training procedure.

tf.losses.huber_loss(
    labels, predictions, weights=1.0, delta=1.0, scope=None,
    loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)

For each value x in error=labels-predictions, the following is calculated:

  0.5 * x^2                  if |x| <= d
  0.5 * d^2 + d * (|x| - d)  if |x| > d

where d is delta.

See: https://en.wikipedia.org/wiki/Huber_loss

weights acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weights is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the weights vector. If the shape of weights matches the shape of predictions, then the loss of each measurable element of predictions is scaled by the corresponding value of weights.

labels The ground truth output tensor, same dimensions as 'predictions'.
predictions The predicted outputs.
weights Optional Tensor whose rank is either 0, or the same rank as labels, and must be broadcastable to labels (i.e., all dimensions must be either 1, or the same as the corresponding losses dimension).
delta float, the point where the huber loss function changes from a quadratic to linear.
scope The scope for the operations performed in computing the loss.
loss_collection collection to which the loss will be added.
reduction Type of reduction to apply to loss.

Weighted loss float Tensor. If reduction is NONE, this has the same shape as labels; otherwise, it is scalar.

ValueError If the shape of predictions doesn't match that of labels or if the shape of weights is invalid. Also if labels or predictions is None.

Eager Compatibility

The loss_collection argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a tf.keras.Model.