tf.contrib.metrics.streaming_accuracy()

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

Calculates how often predictions matches labels.

The streaming_accuracy function creates two local variables, total and count that are used to compute the frequency with which predictions matches labels. This frequency is ultimately returned as accuracy: 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 accuracy. Internally, an is_correct operation computes a Tensor with elements 1.0 where the corresponding elements of predictions and labels match and 0.0 otherwise. Then update_op increments total with the reduced sum of the product of weights and is_correct, 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: The predicted values, a Tensor of any shape.
• labels: The ground truth values, a Tensor whose shape matches predictions.
• weights: An optional Tensor whose shape is broadcastable to predictions.
• metrics_collections: An optional list of collections that accuracy 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:

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

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.