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

tf.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` . |

`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. |

The `loss_collection`

argument is ignored when executing eagerly. Consider holding on to the return value or collecting losses via a `tf.keras.Model`

.

© 2020 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/versions/r1.15/api_docs/python/tf/losses/mean_pairwise_squared_error