# tf.keras.metrics.sparse_categorical_crossentropy

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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#L2304-L2360) |

Computes the sparse categorical crossentropy loss.

#### View aliases


**Main aliases**

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

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

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

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    tf.keras.metrics.sparse_categorical_crossentropy(
        y_true, y_pred, from_logits=False, axis=-1, ignore_class=None
    )

#### Standalone usage:

    y_true = [1, 2]
    y_pred = [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]
    loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred)
    assert loss.shape == (2,)
    loss.numpy()
    array([0.0513, 2.303], dtype=float32)

    y_true = [[[ 0,  2],
               [-1, -1]],
              [[ 0,  2],
               [-1, -1]]]
    y_pred = [[[[1.0, 0.0, 0.0], [0.0, 0.0, 1.0]],
                [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]],
              [[[1.0, 0.0, 0.0], [0.0, 0.5, 0.5]],
               [[0.2, 0.5, 0.3], [0.0, 1.0, 0.0]]]]
    loss = tf.keras.losses.sparse_categorical_crossentropy(
      y_true, y_pred, ignore_class=-1)
    loss.numpy()
    array([[[2.3841855e-07, 2.3841855e-07],
            [0.0000000e+00, 0.0000000e+00]],
           [[2.3841855e-07, 6.9314730e-01],
            [0.0000000e+00, 0.0000000e+00]]], dtype=float32)

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| ## Args ||
|---|---|
| `y_true` | Ground truth values. |
| `y_pred` | The predicted values. |
| `from_logits` | Whether `y_pred` is expected to be a logits tensor. By default, we assume that `y_pred` encodes a probability distribution. |
| `axis` | Defaults to -1. The dimension along which the entropy is computed. |
| `ignore_class` | Optional integer. The ID of a class to be ignored during loss computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (`ignore_class=None`), all classes are considered. |

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| ## Returns ||
|---|---|
| Sparse categorical crossentropy loss value. ||

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