TensorFlow 2 version | View source on GitHub |
Computes the (weighted) mean of the given values.
tf.keras.metrics.Mean(
name='mean', dtype=None
)
For example, if values is [1, 3, 5, 7] then the mean is 4. If the weights were specified as [1, 1, 0, 0] then the mean would be 2.
This metric creates two 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
.
If sample_weight
is None
, weights default to 1.
Use sample_weight
of 0 to mask values.
Usage:
m = tf.keras.metrics.Mean()
m.update_state([1, 3, 5, 7])
print('Final result: ', m.result().numpy()) # Final result: 4.0
Usage with tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.add_metric(tf.keras.metrics.Mean(name='mean_1')(outputs))
model.compile('sgd', loss='mse')
Args | |
---|---|
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
Methods
reset_states
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
result()
Computes and returns the metric value tensor.
Result computation is an idempotent operation that simply calculates the metric value using the state variables.
update_state
update_state(
values, sample_weight=None
)
Accumulates statistics for computing the reduction metric.
For example, if values
is [1, 3, 5, 7] and reduction=SUM_OVER_BATCH_SIZE,
then the value of result()
is 4. If the sample_weight
is specified as
[1, 1, 0, 0] then value of result()
would be 2.
Args | |
---|---|
values
|
Per-example value. |
sample_weight
|
Optional weighting of each example. Defaults to 1. |
Returns | |
---|---|
Update op. |