/TensorFlow 2.3

# tf.keras.metrics.FalsePositives

Calculates the number of false positives.

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.

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`). 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_states()
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

### `reset_states`

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Resets all of the metric state variables.

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

### `result`

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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`

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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.