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TensorFlow 1 version
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View source on GitHub
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Calculates the number of true negatives.
Inherits From: Metric, Layer, Module
tf.keras.metrics.TrueNegatives(
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
true negatives. This metric creates one local variable, accumulator
that is used to keep track of the number of true negatives.
If sample_weight is None, weights default to 1.
Use sample_weight of 0 to mask values.
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. |
Standalone usage:
m = tf.keras.metrics.TrueNegatives()m.update_state([0, 1, 0, 0], [1, 1, 0, 0])m.result().numpy()2.0
m.reset_states()m.update_state([0, 1, 0, 0], [1, 1, 0, 0], sample_weight=[0, 0, 1, 0])m.result().numpy()1.0
Usage with compile() API:
model.compile(optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.TrueNegatives()])
Methods
reset_states
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
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
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.
|
| Returns | |
|---|---|
| Update op. |