tf.losses.mean_squared_error(
labels,
predictions,
weights=1.0,
scope=None,
loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
Defined in tensorflow/python/ops/losses/losses_impl.py
.
Adds a Sum-of-Squares loss to the training procedure.
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
: OptionalTensor
whose rank is either 0, or the same rank aslabels
, and must be broadcastable tolabels
(i.e., all dimensions must be either1
, or the same as the correspondinglosses
dimension).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 ofpredictions
doesn't match that oflabels
or if the shape ofweights
is invalid. Also iflabels
orpredictions
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
.