# tf.keras.metrics.FalsePositives

Calculates the number of false positives.

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

### 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.

#### 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 tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.FalsePositives()])
``````

#### 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.

## Methods

### `reset_states`

View source

``````reset_states()
``````

Resets all of the metric state variables.

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

### `result`

View source

``````result()
``````

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

``````update_state(
y_true, y_pred, sample_weight=None
)
``````

Accumulates the given confusion matrix condition 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`.

Update op.