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Computes the mean absolute error between the labels and predictions.


The mean_absolute_error function creates two local variables, total and count that are used to compute the mean absolute error. This average is weighted by weights, and it is ultimately returned as mean_absolute_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_absolute_error. Internally, an absolute_errors operation computes the absolute value of the differences between predictions and labels. Then update_op increments total with the reduced sum of the product of weights and absolute_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.


  • labels: A Tensor of the same shape as predictions.
  • predictions: A Tensor of arbitrary 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 mean_absolute_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.


  • mean_absolute_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_absolute_error.


  • 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.
  • RuntimeError: If eager execution is enabled.