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tf.keras.metrics.FalseNegatives

TensorFlow 2.0 version View source on GitHub

Class FalseNegatives

Calculates the number of false negatives.

Aliases:

  • Class tf.compat.v1.keras.metrics.FalseNegatives
  • Class tf.compat.v2.keras.metrics.FalseNegatives
  • Class tf.compat.v2.metrics.FalseNegatives

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

If sample_weight is given, calculates the sum of the weights of false negatives. This metric creates one local variable, accumulator that is used to keep track of the number of false negatives.

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

Usage:

m = tf.keras.metrics.FalseNegatives()
m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
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.FalseNegatives()])

__init__

View source

__init__(
    thresholds=None,
    name=None,
    dtype=None
)

Creates a FalseNegatives instance.

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()

result

View source

result()

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.

Returns:

Update op.