tf.metrics.mean_tensor

tf.metrics.mean_tensor(
    values,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Defined in tensorflow/python/ops/metrics_impl.py.

Computes the element-wise (weighted) mean of the given tensors.

In contrast to the mean function which returns a scalar with the mean, this function returns an average tensor with the same shape as the input tensors.

The mean_tensor function creates two local variables, total_tensor and count_tensor that are used to compute the average of values. This average is ultimately returned as mean which is 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. update_op increments total with the reduced sum of the product of values and weights, 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:

• values: A Tensor of arbitrary dimensions.
• weights: Optional Tensor whose rank is either 0, or the same rank as values, and must be broadcastable to values (i.e., all dimensions must be either 1, or the same as the corresponding values dimension).
• metrics_collections: An optional list of collections that mean 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: A float 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_value.

Raises:

• ValueError: If weights is not None and its shape doesn't match values, or if either metrics_collections or updates_collections are not a list or tuple.
• RuntimeError: If eager execution is enabled.