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|
Adds a Huber Loss term to the training procedure.
tf.compat.v1.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.
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.
Args | |
|---|---|
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. |
Returns | |
|---|---|
Weighted loss float Tensor. If reduction is NONE, this has the same
shape as labels; otherwise, it is scalar.
|
Raises | |
|---|---|
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.