# tf.contrib.losses.mean_squared_error

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/losses/python/losses/loss_ops.py#L486-L518) |

Adds a Sum-of-Squares loss to the training procedure. (deprecated)

    tf.contrib.losses.mean_squared_error(
        predictions, labels=None, weights=1.0, scope=None
    )

> [!WARNING]
> **Warning:** THIS FUNCTION IS DEPRECATED. It will be removed after 2016-12-30. Instructions for updating: Use tf.losses.mean_squared_error instead.

`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. If the shape of
`weights` matches the shape of `predictions`, then the loss of each
measurable element of `predictions` is scaled by the corresponding value of
`weights`.

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| ## Args ||
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| `predictions` | The predicted outputs. |
| `labels` | The ground truth output tensor, same dimensions as '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. |

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| ## Returns ||
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| A scalar `Tensor` representing the loss value. ||

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| ## Raises ||
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| `ValueError` | If the shape of `predictions` doesn't match that of `labels` or if the shape of `weights` is invalid. |

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