tf.contrib.metrics.streaming_mean_relative_error()

tf.contrib.metrics.streaming_mean_relative_error(predictions, labels, normalizer, weights=None, metrics_collections=None, updates_collections=None, name=None)

Computes the mean relative error by normalizing with the given values.

The streaming_mean_relative_error function creates two local variables, total and count that are used to compute the mean relative absolute error. This average is weighted by weights, and it is ultimately returned as mean_relative_error: an idempotent operation that simply divides total by count.

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_reative_error. Internally, a relative_errors operation divides the absolute value of the differences between predictions and labels by the normalizer. Then update_op increments total with the reduced sum of the product of weights and relative_errors, and it increments count with the reduced sum of weights.

If weights is None, weights default to 1. Use weights of 0 to mask values.

Args:

• predictions: A Tensor of arbitrary shape.
• labels: A Tensor of the same shape as predictions.
• normalizer: A Tensor of the same shape as predictions.
• weights: An optional Tensor whose shape is broadcastable to predictions.
• metrics_collections: An optional list of collections that mean_relative_error should be added to.
• updates_collections: An optional list of collections that update_op should be added to.
• name: An optional variable_scope name.

Returns:

• mean_relative_error: A tensor representing the current mean, the value of total divided by count.
• update_op: An operation that increments the total and count variables appropriately and whose value matches mean_relative_error.

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.