tf.compat.v1.metrics.mean_cosine

Calculate per-step mean Intersection-Over-Union (mIOU).

tf.compat.v1.metrics.mean_iou(
    labels, predictions, num_classes, weights=None, metrics_collections=None,
    updates_collections=None, name=None
)

Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative). The predictions are accumulated in a confusion matrix, weighted by weights, and mIOU is then calculated from it.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the mean_iou.

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 a confusion matrix of dimension = [num_classes, 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_iou` should be added to.
`updates_collections`	An optional list of collections `update_op` should be added to.
`name`	An optional variable_scope name.

Returns
`mean_iou`	A `Tensor` representing the mean intersection-over-union.
`update_op`	An operation that increments the confusion matrix.

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_cosine_distance

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tf.compat / v1 / v1.metrics.mean_cosine_distance

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