tf.keras.metrics.FalseNegatives

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Class FalseNegatives

Calculates the number of false negatives.

Aliases:

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__

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__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.

__new__

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__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

Methods

tf.keras.metrics.FalseNegatives.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.

tf.keras.metrics.FalseNegatives.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.

tf.keras.metrics.FalseNegatives.update_state

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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.