# tf.keras.metrics.sparse_categorical_accuracy

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.14.0/keras/metrics/accuracy_metrics.py#L431-L465) |

Calculates how often predictions match integer labels.

#### View aliases


**Main aliases**

[`tf.metrics.sparse_categorical_accuracy`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_categorical_accuracy)

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

#### Standalone usage:

    y_true = [2, 1]
    y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]
    m = tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
    assert m.shape == (2,)
    m.numpy()
    array([0., 1.], dtype=float32)

You can provide logits of classes as `y_pred`, since argmax of
logits and probabilities are same.

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| ## Args ||
|---|---|
| `y_true` | Integer ground truth values. |
| `y_pred` | The prediction values. |

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| ## Returns ||
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| Sparse categorical accuracy values. ||

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