# tf.losses.mean_pairwise_squared_error

tf.losses.mean_pairwise_squared_error(
labels,
predictions,
weights=1.0,
scope=None,
loss_collection=tf.GraphKeys.LOSSES
)


Adds a pairwise-errors-squared loss to the training procedure.

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.
• predictions: The predicted outputs, a tensor of size [batch_size, d0, .. dN] where N+1 is the total number of dimensions in predictions.
• weights: Coefficients for the loss a scalar, a tensor of shape [batch_size] or a tensor whose shape matches predictions.
• 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.

#### 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.