tf.compat.v1.metrics.mean

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`</td> <td> An optional list of collections`update_op`should 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.

TensorFlow

tf.compat / v1 / v1.metrics.mean_iou

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TensorFlow

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tf.compat / v1 / v1.metrics.mean_iou

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