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))