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# tf.keras.losses.huber

Computes Huber loss value.

For each value x in `error = y_true - y_pred`:

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

where d is `delta`. See: https://en.wikipedia.org/wiki/Huber_loss

`y_true` tensor of true targets.
`y_pred` tensor of predicted targets.
`delta` A float, the point where the Huber loss function changes from a quadratic to linear.

Tensor with one scalar loss entry per sample.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]