TensorFlow 1 version | View source on GitHub |
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
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(
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. |