tf.compat.v1.metrics.mean

Computes the (weighted) mean of the given values.

tf.compat.v1.metrics.mean(
    values,
    weights=None,
    metrics_collections=None,
    updates_collections=None,
    name=None
)

Migrate to TF2

tf.compat.v1.metrics.mean is not compatible with eager execution or tf.function. Please use tf.keras.metrics.Mean instead for TF2 migration. After instantiating a tf.keras.metrics.Mean object, you can first call the update_state() method to record the new values, and then call the result() method to get the mean eagerly. You can also attach it to a Keras model with the add_metric method. Please refer to the migration guide for more details.

Structural Mapping to TF2

Before:

mean, update_op = tf.compat.v1.metrics.mean(
  values=values,
  weights=weights,
  metrics_collections=metrics_collections,
  update_collections=update_collections,
  name=name)

After:

 m = tf.keras.metrics.Mean(
   name=name)

 m.update_state(
   values=values,
   sample_weight=weights)

 mean = m.result()

How to Map Arguments

TF1 Arg Name	TF2 Arg Name	Note
`values`	`values`	In `update_state()` method
`weights`	`sample_weight`	In `update_state()` method
`metrics_collections`	Not supported	Metrics should be tracked explicitly or with Keras APIs, for example, add_metric, instead of via collections
`updates_collections`	Not supported	-
`name`	`name`	In constructor

Before & After Usage Example

Before:

g = tf.Graph()
with g.as_default():
  values = [1, 2, 3]
  mean, update_op = tf.compat.v1.metrics.mean(values)
  global_init = tf.compat.v1.global_variables_initializer()
  local_init = tf.compat.v1.local_variables_initializer()
sess = tf.compat.v1.Session(graph=g)
sess.run([global_init, local_init])
sess.run(update_op)
sess.run(mean)
2.0

After:

m = tf.keras.metrics.Mean()
m.update_state([1, 2, 3])
m.result().numpy()
2.0

# Used within Keras model
model.add_metric(tf.keras.metrics.Mean()(values))

Description

The mean function creates two local variables, total and count 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 `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.