tf.compat.v1.losses.mean_squared_error
Adds a Sum-of-Squares loss to the training procedure.
tf.compat.v1.losses.mean_squared_error(
labels, predictions, weights=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
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).
|
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
.
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Last updated 2020-10-01 UTC.
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