Class MetricSpec

tensorflow/contrib/learn/python/learn/metric_spec.py:229–442

MetricSpec connects a model to metric functions. THIS CLASS IS DEPRECATED. See [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md) for general migration instructions. The MetricSpec class contains all information necessary to connect the output o

class MetricSpec(object):
  """MetricSpec connects a model to metric functions.

  THIS CLASS IS DEPRECATED. See
  [contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
  for general migration instructions.

  The MetricSpec class contains all information necessary to connect the
  output of a `model_fn` to the metrics (usually, streaming metrics) that are
  used in evaluation.

  It is passed in the `metrics` argument of `Estimator.evaluate`. The
  `Estimator` then knows which predictions, labels, and weight to use to call a
  given metric function.

  When building the ops to run in evaluation, an `Estimator` will call
  `create_metric_ops`, which will connect the given `metric_fn` to the model
  as detailed in the docstring for `create_metric_ops`, and return the metric.

  Example:

  Assuming a model has an input function which returns inputs containing
  (among other things) a tensor with key "input_key", and a labels dictionary
  containing "label_key". Let's assume that the `model_fn` for this model
  returns a prediction with key "prediction_key".

  In order to compute the accuracy of the "prediction_key" prediction, we
  would add

  ```
  "prediction accuracy": MetricSpec(metric_fn=prediction_accuracy_fn,
                                    prediction_key="prediction_key",
                                    label_key="label_key")
  ```

  to the metrics argument to `evaluate`. `prediction_accuracy_fn` can be either
  a predefined function in metric_ops (e.g., `streaming_accuracy`) or a custom
  function you define.

  If we would like the accuracy to be weighted by "input_key", we can add that
  as the `weight_key` argument.

  ```
  "prediction accuracy": MetricSpec(metric_fn=prediction_accuracy_fn,
                                    prediction_key="prediction_key",
                                    label_key="label_key",
                                    weight_key="input_key")
  ```

  An end-to-end example is as follows:

  ```
  estimator = tf.contrib.learn.Estimator(...)
  estimator.fit(...)
  _ = estimator.evaluate(
      input_fn=input_fn,
      steps=1,
      metrics={