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Computes the Intersection-Over-Union metric for class 0 and/or 1.
tf.keras.metrics.BinaryIoU(
target_class_ids=(0, 1), threshold=0.5, name=None, dtype=None
)
Formula:
iou = true_positives / (true_positives + false_positives + false_negatives)
Intersection-Over-Union is a common evaluation metric for semantic image segmentation.
To compute IoUs, the predictions are accumulated in a confusion matrix,
weighted by sample_weight
and the metric is then calculated from it.
If sample_weight
is None
, weights default to 1.
Use sample_weight
of 0 to mask values.
This class can be used to compute IoUs for a binary classification task
where the predictions are provided as logits. First a threshold
is applied
to the predicted values such that those that are below the threshold
are
converted to class 0 and those that are above the threshold
are converted
to class 1.
IoUs for classes 0 and 1 are then computed, the mean of IoUs for the classes
that are specified by target_class_ids
is returned.
Example:
Example:
m = keras.metrics.BinaryIoU(target_class_ids=[0, 1], threshold=0.3)
m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7])
m.result()
0.33333334
m.reset_state()
m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7],
sample_weight=[0.2, 0.3, 0.4, 0.1])
# cm = [[0.2, 0.4],
# [0.3, 0.1]]
# sum_row = [0.6, 0.4], sum_col = [0.5, 0.5],
# true_positives = [0.2, 0.1]
# iou = [0.222, 0.125]
m.result()
0.17361112
Usage with compile()
API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[keras.metrics.BinaryIoU(
target_class_ids=[0],
threshold=0.5
)]
)
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 intersection-over-union via the confusion matrix.
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 confusion matrix statistics.
Before the confusion matrix is updated, the predicted values are
thresholded to be:
0 for values that are smaller than the threshold
1 for values that are larger or equal to the threshold
Args | |
---|---|
y_true
|
The ground truth values. |
y_pred
|
The predicted values. |
sample_weight
|
Optional weighting of each example. Can
be a Tensor whose rank is either 0, or the same as y_true ,
and must be broadcastable to y_true . Defaults to 1 .
|
Returns | |
---|---|
Update op. |
__call__
__call__(
*args, **kwargs
)
Call self as a function.