# tf.compat.v1.losses.get_total_loss

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/losses/util.py#L234-L263) |

Returns a tensor whose value represents the total loss.

    tf.compat.v1.losses.get_total_loss(
        add_regularization_losses=True, name='total_loss', scope=None
    )

In particular, this adds any losses you have added with `tf.add_loss()` to
any regularization losses that have been added by regularization parameters
on layers constructors e.g. `tf.layers`. Be very sure to use this if you
are constructing a loss_op manually. Otherwise regularization arguments
on `tf.layers` methods will not function.

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| ## Args ||
|---|---|
| `add_regularization_losses` | A boolean indicating whether or not to use the regularization losses in the sum. |
| `name` | The name of the returned tensor. |
| `scope` | An optional scope name for filtering the losses to return. Note that this filters the losses added with `tf.add_loss()` as well as the regularization losses to that scope. |

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| ## Returns ||
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| A `Tensor` whose value represents the total loss. ||

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| ## Raises ||
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| `ValueError` | if `losses` is not iterable. |

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