tf.keras.metrics.MeanAbsoluteError

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Computes the mean absolute error between the labels and predictions.

tf.keras.metrics.MeanAbsoluteError(
    name='mean_absolute_error', dtype=None
)

For example, if y_true is [0., 0., 1., 1.], and y_pred is [1., 1., 1., 0.] the mean absolute error is 3/4 (0.75).

Usage:

>>> m = MeanAbsoluteError()
>>> _ = m.update_state([0., 0., 1., 1.], [1., 1., 1., 0.])
>>> m.result().numpy()
0.75

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.MeanAbsoluteError()])

Args:

• fn: The metric function to wrap, with signature fn(y_true, y_pred, **kwargs).
• name: (Optional) string name of the metric instance.
• dtype: (Optional) data type of the metric result.
• **kwargs: The keyword arguments that are passed on to fn.

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.

y_true and y_pred should have the same shape.

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.