# tf.keras.metrics.SparseCategoricalCrossentropy

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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/probabilistic_metrics.py#L240-L318) |

Computes the crossentropy metric between the labels and predictions.

Inherits From: [`MeanMetricWrapper`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/metrics/MeanMetricWrapper), [`Mean`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/metrics/Mean), [`Metric`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/metrics/Metric), [`Layer`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Module)

#### View aliases


**Main aliases**

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

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    tf.keras.metrics.SparseCategoricalCrossentropy(
        name: str = 'sparse_categorical_crossentropy',
        dtype: Optional[Union[str, https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/dtypes/DType]] = None,
        from_logits: bool = False,
        ignore_class: Optional[int] = None,
        axis: int = -1
    )

Use this crossentropy metric when there are two or more label classes.
We expect labels to be provided as integers. If you want to provide labels
using `one-hot` representation, please use `CategoricalCrossentropy` metric.
There should be `# classes` floating point values per feature for `y_pred`
and a single floating point value per feature for `y_true`.

In the snippet below, there is a single floating point value per example for
`y_true` and `# classes` floating pointing values per example for `y_pred`.
The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is
`[batch_size, num_classes]`.

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| ## Args ||
|---|---|
| `name` | (Optional) string name of the metric instance. |
| `dtype` | (Optional) data type of the metric result. |
| `from_logits` | (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution. |
| `ignore_class` | Optional integer. The ID of a class to be ignored during metric 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. |
| `axis` | (Optional) The dimension along which entropy is computed. Defaults to `-1`. |

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#### Standalone usage:

    # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
    # logits = log(y_pred)
    # softmax = exp(logits) / sum(exp(logits), axis=-1)
    # softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
    # xent = -sum(y * log(softmax), 1)
    # log(softmax) = [[-2.9957, -0.0513, -16.1181],
    #                [-2.3026, -0.2231, -2.3026]]
    # y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]
    # xent = [0.0513, 2.3026]
    # Reduced xent = (0.0513 + 2.3026) / 2
    m = tf.keras.metrics.SparseCategoricalCrossentropy()
    m.update_state([1, 2],
                   [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
    m.result().numpy()
    1.1769392

    m.reset_state()
    m.update_state([1, 2],
                   [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
                   sample_weight=tf.constant([0.3, 0.7]))
    m.result().numpy()
    1.6271976

Usage with `compile()` API:

    model.compile(
      optimizer='sgd',
      loss='sparse_categorical_crossentropy',
      metrics=[tf.keras.metrics.SparseCategoricalCrossentropy()])

## Methods

### `merge_state`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/metrics/base_metric.py#L288-L326)

    merge_state(
        metrics
    )

Merges the state from one or more metrics.

This method can be used by distributed systems to merge the state
computed by different metric instances. Typically the state will be
stored in the form of the metric's weights. For example, a
tf.keras.metrics.Mean metric contains a list of two weight values: a
total and a count. If there were two instances of a
tf.keras.metrics.Accuracy that each independently aggregated partial
state for an overall accuracy calculation, these two metric's states
could be combined as follows:

    m1 = tf.keras.metrics.Accuracy()
    _ = m1.update_state([[1], [2]], [[0], [2]])

    m2 = tf.keras.metrics.Accuracy()
    _ = m2.update_state([[3], [4]], [[3], [4]])

    m2.merge_state([m1])
    m2.result().numpy()
    0.75

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| Args ||
|---|---|
| `metrics` | an iterable of metrics. The metrics must have compatible state. |

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| Raises ||
|---|---|
| `ValueError` | If the provided iterable does not contain metrics matching the metric's required specifications. |

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### `reset_state`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/metrics/base_metric.py#L249-L265)

    reset_state()

Resets all of the metric state variables.

This function is called between epochs/steps,
when a metric is evaluated during training.

### `result`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/metrics/base_metric.py#L551-L563)

    result()

Computes and returns the scalar metric value tensor or a dict of scalars.

Result computation is an idempotent operation that simply calculates the
metric value using the state variables.

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| Returns ||
|---|---|
| A scalar tensor, or a dictionary of scalar tensors. ||

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### `update_state`

[View source](https://github.com/keras-team/keras/tree/v2.14.0/keras/metrics/base_metric.py#L686-L728)

    update_state(
        y_true, y_pred, sample_weight=None
    )

Accumulates metric statistics.

For sparse categorical metrics, the shapes of `y_true` and `y_pred` are
different.

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| Args ||
|---|---|
| `y_true` | Ground truth label values. shape = `[batch_size, d0, .. dN-1]` or shape = `[batch_size, d0, .. dN-1, 1]`. |
| `y_pred` | The predicted probability values. shape = `[batch_size, d0, .. dN]`. |
| `sample_weight` | Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)). |

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| Returns ||
|---|---|
| Update op. ||

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