tf.contrib.metrics.streaming_accuracy

Calculates how often predictions matches labels. (deprecated)

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

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please switch to tf.metrics.accuracy. Note that the order of the labels and predictions arguments has been switched.

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	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 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.