# tf.losses.log_loss

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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/python/ops/losses/losses_impl.py#L443-L496) |

Adds a Log Loss term to the training procedure.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.losses.log_loss`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/losses/log_loss)

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    tf.losses.log_loss(
        labels, predictions, weights=1.0, epsilon=1e-07, scope=None,
        loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
    )

`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 ||
|---|---|
| `labels` | The ground truth output tensor, same dimensions as 'predictions'. |
| `predictions` | The predicted outputs. |
| `weights` | Optional `Tensor` whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). |
| `epsilon` | A small increment to add to avoid taking a log of zero. |
| `scope` | The scope for the operations performed in computing the loss. |
| `loss_collection` | collection to which the loss will be added. |
| `reduction` | Type of reduction to apply to loss. |

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| ## Returns ||
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| Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same shape as `labels`; otherwise, it is scalar. ||

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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. Also if `labels` or `predictions` is None. |

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#### Eager Compatibility

The `loss_collection` argument is ignored when executing eagerly. Consider
holding on to the return value or collecting losses via a [`tf.keras.Model`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/keras/Model).