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Computes mean Intersection-Over-Union metric for one-hot encoded labels.
Inherits From: MeanIoU
, IoU
, Metric
tf.keras.metrics.OneHotMeanIoU(
num_classes,
name=None,
dtype=None,
ignore_class=None,
sparse_y_pred=False,
axis=-1
)
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 the mean IoU for multi-class
classification tasks where the labels are one-hot encoded (the last axis
should have one dimension per class). Note that the predictions should also
have the same shape. To compute the mean IoU, first the labels and
predictions are converted back into integer format by taking the argmax over
the class axis. Then the same computation steps as for the base MeanIoU
class apply.
Note, if there is only one channel in the labels and predictions, this class
is the same as class MeanIoU
. In this case, use MeanIoU
instead.
Also, make sure that num_classes
is equal to the number of classes in the
data, to avoid a "labels out of bound" error when the confusion matrix is
computed.
Example:
Example:
y_true = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])
y_pred = np.array([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1],
[0.1, 0.4, 0.5]])
sample_weight = [0.1, 0.2, 0.3, 0.4]
m = keras.metrics.OneHotMeanIoU(num_classes=3)
m.update_state(
y_true=y_true, y_pred=y_pred, sample_weight=sample_weight)
# cm = [[0, 0, 0.2+0.4],
# [0.3, 0, 0],
# [0, 0, 0.1]]
# sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1]
# true_positives = [0, 0, 0.1]
# single_iou = true_positives / (sum_row + sum_col - true_positives))
# mean_iou = (0 + 0 + 0.1 / (0.7 + 0.1 - 0.1)) / 3
m.result()
0.048
Usage with compile()
API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[keras.metrics.OneHotMeanIoU(num_classes=3)])
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.
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.