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tf.keras.metrics.FalsePositives

Calculates the number of false positives.

Inherits From: `Metric`, `Layer`, `Module`

Used in the notebooks

Used in the tutorials

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

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

`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`). 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.

Standalone usage:

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

Methods

`merge_state`

View source

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`
```

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

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

`reset_state`

View source

Resets all of the metric state variables.

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

`result`

View source

Computes and returns the metric value tensor.

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

`update_state`

View source

Accumulates the metric statistics.

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`.

Returns
Update op.

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