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TensorFlow 2 version View source on GitHub

Computes the mean relative error by normalizing with the given values.

Inherits From: Mean

This metric creates two local variables, total and count that are used to compute the mean relative absolute error. This average 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.


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
print('Final result: ', m.result().numpy())  # Final result: 1.25

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
  metrics=[tf.keras.metrics.MeanRelativeError(normalizer=[1, 3])])

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.



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Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.


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Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.


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Accumulates metric statistics.

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.

Update op.