# tf.keras.metrics.OneHotMeanIoU

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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/metrics/iou_metrics.py#L649-L757) |

Computes mean Intersection-Over-Union metric for one-hot encoded labels.

Inherits From: [`MeanIoU`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/MeanIoU), [`IoU`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/IoU), [`Metric`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/metrics/Metric), [`Layer`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/Module)

#### View aliases


**Main aliases**

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

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

\`tf.compat.v1.keras.metrics.OneHotMeanIoU\`

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    tf.keras.metrics.OneHotMeanIoU(
        num_classes: int,
        name: str = None,
        dtype: Optional[Union[str, https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/dtypes/DType]] = None,
        ignore_class: Optional[int] = None,
        sparse_y_pred: bool = False,
        axis: int = -1
    )

General definition and computation:

Intersection-Over-Union is a common evaluation metric for semantic image
segmentation.

For an individual class, the IoU metric is defined as follows:

    iou = true_positives / (true_positives + false_positives + false_negatives)

To compute IoUs, the predictions are accumulated in a confusion matrix,
weighted by `sample_weight` and the metric is then calculated from it.

If `sample_weight` is `None`, weights default to 1.
Use `sample_weight` of 0 to mask values.

This class can be used to compute the mean IoU for multi-class
classification tasks where the labels are one-hot encoded (the last axis
should have one dimension per class). Note that the predictions should also
have the same shape. To compute the mean IoU, first the labels and
predictions are converted back into integer format by taking the argmax over
the class axis. Then the same computation steps as for the base `MeanIoU`
class apply.

Note, if there is only one channel in the labels and predictions, this class
is the same as class `MeanIoU`. In this case, use `MeanIoU` instead.

Also, make sure that `num_classes` is equal to the number of classes in the
data, to avoid a "labels out of bound" error when the confusion matrix is
computed.

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| ## Args ||
|---|---|
| `num_classes` | The possible number of labels the prediction task can have. A confusion matrix of shape `(num_classes, num_classes)` will be allocated to accumulate predictions from which the metric is calculated. |
| `name` | (Optional) string name of the metric instance. |
| `dtype` | (Optional) data type of the metric result. |
| `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. |
| `sparse_y_pred` | Whether predictions are encoded using natural numbers or probability distribution vectors. If `False`, the [`tf.argmax`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/argmax) function will be used to determine each sample's most likely associated label. |
| `axis` | (Optional) Defaults to -1. The dimension containing the logits. |

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#### Standalone usage:

    y_true = tf.constant([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])
    y_pred = tf.constant([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1],
                          [0.1, 0.4, 0.5]])
    sample_weight = [0.1, 0.2, 0.3, 0.4]
    m = tf.keras.metrics.OneHotMeanIoU(num_classes=3)
    m.update_state(
        y_true=y_true, y_pred=y_pred, sample_weight=sample_weight)
    # cm = [[0, 0, 0.2+0.4],
    #       [0.3, 0, 0],
    #       [0, 0, 0.1]]
    # sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1]
    # true_positives = [0, 0, 0.1]
    # single_iou = true_positives / (sum_row + sum_col - true_positives))
    # mean_iou = (0 + 0 + 0.1 / (0.7 + 0.1 - 0.1)) / 3
    m.result().numpy()
    0.048

Usage with `compile()` API:

    model.compile(
      optimizer='sgd',
      loss='mse',
      metrics=[tf.keras.metrics.OneHotMeanIoU(num_classes=3)])

## Methods

### `merge_state`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/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/v2.13.1/keras/metrics/iou_metrics.py#L149-L152)

    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/v2.13.1/keras/metrics/iou_metrics.py#L264-L293)

    result()

Compute the intersection-over-union via the confusion matrix.

### `update_state`

[View source](https://github.com/keras-team/keras/tree/v2.13.1/keras/metrics/iou_metrics.py#L97-L147)

    update_state(
        y_true, y_pred, sample_weight=None
    )

Accumulates the confusion matrix statistics.

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| Args ||
|---|---|
| `y_true` | The ground truth values. |
| `y_pred` | The predicted values. |
| `sample_weight` | Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`. |

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

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