# tf.losses.get_total_loss

<br />

|---|
| [![](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/util.py#L242-L271) |

Returns a tensor whose value represents the total loss.

#### View aliases


**Compat aliases for migration**

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

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

<br />

    tf.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`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/layers). Be very sure to use this if you
are constructing a loss_op manually. Otherwise regularization arguments
on [`tf.layers`](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/layers) methods will not function.

<br />

<br />

<br />

| ## 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. |

<br />

<br />

<br />

<br />

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

<br />

<br />

<br />

<br />

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

<br />