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# 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

For example, if y_true is [0, 1, 0, 0] and y_pred is [0, 0, 1, 1] then the false positives value is 2. If the weights were specified as [0, 0, 1, 0] then the false positives value would be 1.

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])
print('Final result: ', m.result().numpy())  # Final result: 2

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.