| 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. |
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])])
reset_statesreset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
resultresult()
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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/metrics/MeanRelativeError