tf.keras.metrics.PrecisionAtRecall

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Computes the precision at a given recall.

tf.keras.metrics.PrecisionAtRecall(
    recall, num_thresholds=200, name=None, dtype=None
)

This metric creates four local variables, true_positives, true_negatives, false_positives and false_negatives that are used to compute the precision at the given recall. The threshold for the given recall value is computed and used to evaluate the corresponding precision.

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

Usage:

m = tf.keras.metrics.PrecisionAtRecall(0.8, num_thresholds=1)
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
print('Final result: ', m.result().numpy())  # Final result: 1.0

Usage with tf.keras API:

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

Args:

• recall: A scalar value in range [0, 1].
• num_thresholds: (Optional) Defaults to 200. The number of thresholds to use for matching the given recall.
• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.

Methods

reset_states

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reset_states()

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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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

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update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates confusion matrix 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.