Calculates how often predictions matches labels.

Used in the notebooks

Used in the tutorials

The 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.

labels The ground truth values, a Tensor whose shape matches predictions.
predictions The predicted values, a Tensor of any shape.
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 accuracy should be added to.
updates_collections An optional list of collections that update_op should be added to.
name An optional