tf.compat.v1.metrics.mean_per_class_accuracy
Calculates the mean of the per-class accuracies.
tf.compat.v1.metrics.mean_per_class_accuracy(
labels,
predictions,
num_classes,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None
)
Calculates the accuracy for each class, then takes the mean of that.
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates the accuracy of each class and returns
them.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args |
labels
|
A Tensor of ground truth labels with shape [batch size] and of
type int32 or int64 . The tensor will be flattened if its rank > 1.
|
predictions
|
A Tensor of prediction results for semantic labels, whose
shape is [batch size] and type int32 or int64 . The tensor will be
flattened if its rank > 1.
|
num_classes
|
The possible number of labels the prediction task can
have. This value must be provided, since two variables with shape =
[num_classes] will be allocated.
|
weights
|
Optional Tensor whose rank is either 0, or the same rank as
labels , and must be broadcastable to labels (i.e., all dimensions must
be either 1 , or the same as the corresponding labels dimension).
|
metrics_collections
|
An optional list of collections that
mean_per_class_accuracy'
should be added to.
</td>
</tr><tr>
<td> updates_collections<a id="updates_collections"></a>
</td>
<td>
An optional list of collections update_opshould be
added to.
</td>
</tr><tr>
<td> name`
|
An optional variable_scope name.
|
Returns |
mean_accuracy
|
A Tensor representing the mean per class accuracy.
|
update_op
|
An operation that updates the accuracy tensor.
|
Raises |
ValueError
|
If predictions and labels have mismatched shapes, or if
weights is not None and its shape doesn't match predictions , or if
either metrics_collections or updates_collections are not a list or
tuple.
|
RuntimeError
|
If eager execution is enabled.
|
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Last updated 2023-03-17 UTC.
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