tf.keras.metrics.RecallAtPrecision

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Computes the maximally achievable recall at a required precision.

tf.keras.metrics.RecallAtPrecision(
    precision, num_thresholds=200, name=None, dtype=None
)

For a given score-label-distribution the required precision might not be achievable, in this case 0.0 is returned as recall.

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

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

Usage:

m = tf.keras.metrics.RecallAtPrecision(0.8, num_thresholds=1) 
_ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9]) 
m.result().numpy() 
0.5 
m.reset_states() 
_ = m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9], 
                   sample_weight=[1, 0, 0, 1]) 
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.RecallAtPrecision(precision=0.8)])

Args:

  • precision: A scalar value in range [0, 1].
  • num_thresholds: (Optional) Defaults to 200. The number of thresholds to use for matching the given precision.
  • 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.