# tf.keras.metrics.MeanRelativeError

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## Class `MeanRelativeError`

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

Inherits From: `Mean`

### Aliases:

• Class `tf.compat.v1.keras.metrics.MeanRelativeError`
• Class `tf.compat.v2.keras.metrics.MeanRelativeError`
• Class `tf.compat.v2.metrics.MeanRelativeError`
• Class `tf.metrics.MeanRelativeError`

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.

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

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile(
'sgd',
loss='mse',
metrics=[tf.keras.metrics.MeanRelativeError(normalizer=[1, 3])])
``````

## `__init__`

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``````__init__(
normalizer,
name=None,
dtype=None
)
``````

Creates a `MeanRelativeError` instance.

#### 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.

## Methods

### `reset_states`

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``````reset_states()
``````

Resets all of the metric state variables.

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

### `result`

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``````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`

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``````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`.

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