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tf.keras.metrics.MeanRelativeError

TensorFlow 1 version View source on GitHub

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__

View source

__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__

View source

__new__(
    cls,
    *args,
    **kwargs
)

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

Methods

reset_states

View source

reset_states()

Resets all of the metric state variables.

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

result

View source

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

View source

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.