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Computes the mean relative error by normalizing with the given values.
Inherits From: Mean
, Metric
, Layer
, Module
tf.keras.metrics.MeanRelativeError(
normalizer, name=None, dtype=None
)
This metric creates two local variables, total
and count
that are used to
compute the mean relative error. This is weighted by sample_weight
, and
it is ultimately returned as mean_relative_error
:
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.
Args | |
---|---|
normalizer
|
The normalizer values with same shape as predictions. |
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
Standalone usage:
m = tf.keras.metrics.MeanRelativeError(normalizer=[1, 3, 2, 3])
m.update_state([1, 3, 2, 3], [2, 4, 6, 8])
# metric = mean(|y_pred - y_true| / normalizer)
# = mean([1, 1, 4, 5] / [1, 3, 2, 3]) = mean([1, 1/3, 2, 5/3])
# = 5/4 = 1.25
m.result().numpy()
1.25
Usage with compile()
API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanRelativeError(normalizer=[1, 3])])
Methods
merge_state
merge_state(
metrics
)
Merges the state from one or more metrics.
This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:
m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
m2.merge_state([m1])
m2.result().numpy()
0.75
Args | |
---|---|
metrics
|
an iterable of metrics. The metrics must have compatible state. |
Raises | |
---|---|
ValueError
|
If the provided iterable does not contain metrics matching the metric's required specifications. |
reset_state
reset_state()
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 scalar metric value tensor or a dict of scalars.
Result computation is an idempotent operation that simply calculates the metric value using the state variables.
Returns | |
---|---|
A scalar tensor, or a dictionary of scalar tensors. |
update_state
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates metric statistics.
Args | |
---|---|
y_true
|
The ground truth values. |
y_pred
|
The predicted values. |
sample_weight
|
Optional weighting of each example. Defaults to 1. Can be a
Tensor whose rank is either 0, or the same rank as y_true , and must
be broadcastable to y_true .
|
Returns | |
---|---|
Update op. |