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Adds a pairwise-errors-squared loss to the training procedure.
tf.compat.v1.losses.mean_pairwise_squared_error(
labels, predictions, weights=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES
)
Unlike mean_squared_error, which is a measure of the differences between
corresponding elements of predictions and labels,
mean_pairwise_squared_error is a measure of the differences between pairs of
corresponding elements of predictions and labels.
For example, if labels=[a, b, c] and predictions=[x, y, z], there are
three pairs of differences are summed to compute the loss:
loss = [ ((a-b) - (x-y)).^2 + ((a-c) - (x-z)).^2 + ((b-c) - (y-z)).^2 ] / 3
Note that since the inputs are of shape [batch_size, d0, ... dN], the
corresponding pairs are computed within each batch sample but not across
samples within a batch. For example, if predictions represents a batch of
16 grayscale images of dimension [batch_size, 100, 200], then the set of pairs
is drawn from each image, but not across images.
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.
Args | |
|---|---|
labels
|
The ground truth output tensor, whose shape must match the shape of
predictions.
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predictions
|
The predicted outputs, a tensor of size
[batch_size, d0, .. dN] where N+1 is the total number of dimensions in
predictions.
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weights
|
Coefficients for the loss a scalar, a tensor of shape
[batch_size] or a tensor whose shape matches predictions.
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scope
|
The scope for the operations performed in computing the loss. |
loss_collection
|
collection to which the loss will be added. |
Returns | |
|---|---|
A scalar Tensor that returns the weighted loss.
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Raises | |
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ValueError
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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.
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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.