# tf.keras.metrics.hinge

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.13.1/keras/losses.py#L1909-L1938) |

Computes the hinge loss between `y_true` \& `y_pred`.

#### View aliases


**Main aliases**

[`tf.keras.losses.hinge`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/hinge), [`tf.losses.hinge`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/hinge), [`tf.metrics.hinge`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/hinge)
**Compat aliases for migration**

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

\`tf.compat.v1.keras.losses.hinge\`, \`tf.compat.v1.keras.metrics.hinge\`

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    tf.keras.metrics.hinge(
        y_true, y_pred
    )

`loss = mean(maximum(1 - y_true * y_pred, 0), axis=-1)`

#### Standalone usage:

    y_true = np.random.choice([-1, 1], size=(2, 3))
    y_pred = np.random.random(size=(2, 3))
    loss = tf.keras.losses.hinge(y_true, y_pred)
    assert loss.shape == (2,)
    assert np.array_equal(
        loss.numpy(),
        np.mean(np.maximum(1. - y_true * y_pred, 0.), axis=-1))

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| ## Args ||
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| `y_true` | The ground truth values. `y_true` values are expected to be -1 or <br /> 1. If binary (0 or 1) labels are provided they will be converted to -1 or 1. shape = `[batch_size, d0, .. dN]`. |
| `y_pred` | The predicted values. shape = `[batch_size, d0, .. dN]`. |

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| ## Returns ||
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| Hinge loss values. shape = `[batch_size, d0, .. dN-1]`. ||

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