tf.contrib.losses.mean_pairwise_squared_error()

tf.contrib.losses.mean_pairwise_squared_error(*args, **kwargs)

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

THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-01. Instructions for updating: Use mean_pairwise_squared_error.

Unlike the sum_of_squares loss, which is a measure of the differences between corresponding elements of predictions and targets, sum_of_pairwise_squares is a measure of the differences between pairs of corresponding elements of predictions and targets.

For example, if targets=[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 size [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 dimenion [batch_size, 100, 200], then the set of pairs is drawn from each image, but not across images.

weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight 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 weight vector.

Args: predictions: The predicted outputs, a tensor of size [batch_size, d0, .. dN] where N+1 is the total number of dimensions in predictions. targets: The ground truth output tensor, whose shape must match the shape of the predictions tensor. weight: 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.

Returns: A scalar Tensor representing the loss value.

Raises: ValueError: If the shape of predictions doesn't match that of targets or if the shape of weight is invalid.