# tf.keras.metrics.TruePositives

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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/confusion_metrics.py#L287-L345) |

Calculates the number of true positives.

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

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

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

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    tf.keras.metrics.TruePositives(
        thresholds=None, name=None, dtype=None
    )

If `sample_weight` is given, calculates the sum of the weights of
true positives. This metric creates one local variable, `true_positives`
that is used to keep track of the number of true positives.

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

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| ## Args ||
|---|---|
| `thresholds` | (Optional) Defaults to 0.5. A float value, or a Python list/tuple of float threshold values in \[0, 1\]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If used with a loss function that sets `from_logits=True` (i.e. no sigmoid applied to predictions), `thresholds` should be set to 0. One metric value is generated for each threshold value. |
| `name` | (Optional) string name of the metric instance. |
| `dtype` | (Optional) data type of the metric result. |

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#### Standalone usage:

    m = tf.keras.metrics.TruePositives()
    m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
    m.result().numpy()
    2.0

    m.reset_state()
    m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
    m.result().numpy()
    1.0

Usage with `compile()` API:

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

Usage with a loss with `from_logits=True`:

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
                  metrics=[tf.keras.metrics.TruePositives(thresholds=0)])

## 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/confusion_metrics.py#L93-L96)

    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/confusion_metrics.py#L86-L91)

    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/v2.13.1/keras/metrics/confusion_metrics.py#L64-L84)

    update_state(
        y_true, y_pred, sample_weight=None
    )

Accumulates the metric 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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