tf.losses.mean_pairwise_squared_error( labels, predictions, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES )
Defined in tensorflow/python/ops/losses/losses_impl.py
.
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.
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.A scalar Tensor
that returns the weighted loss.
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.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/losses/mean_pairwise_squared_error