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:

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.

__new__

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__new__(
    cls,
    *args,
    **kwargs
)

Create and return a new object. See help(type) for accurate signature.

Methods

tf.keras.metrics.MeanRelativeError.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.

tf.keras.metrics.MeanRelativeError.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.

tf.keras.metrics.MeanRelativeError.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.

Returns:

Update op.