View source on GitHub |
Calculates the number of false negatives.
Inherits From: Metric
tf.keras.metrics.FalseNegatives(
thresholds=None, name=None, dtype=None
)
Used in the notebooks
Used in the tutorials |
---|
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.
Example:
m = keras.metrics.FalseNegatives()
m.update_state([0, 1, 1, 1], [0, 1, 0, 0])
m.result()
2.0
m.reset_state()
m.update_state([0, 1, 1, 1], [0, 1, 0, 0], sample_weight=[0, 0, 1, 0])
m.result()
1.0
Attributes | |
---|---|
dtype
|
|
variables
|
Methods
add_variable
add_variable(
shape, initializer, dtype=None, aggregation='sum', name=None
)
add_weight
add_weight(
shape=(), initializer=None, dtype=None, name=None
)
from_config
@classmethod
from_config( config )
get_config
get_config()
Return the serializable config of the metric.
reset_state
reset_state()
Reset all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
Compute the current metric value.
Returns | |
---|---|
A scalar tensor, or a dictionary of scalar tensors. |
stateless_reset_state
stateless_reset_state()
stateless_result
stateless_result(
metric_variables
)
stateless_update_state
stateless_update_state(
metric_variables, *args, **kwargs
)
update_state
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates the metric 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 .
|
__call__
__call__(
*args, **kwargs
)
Call self as a function.