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tf.keras.metrics.MeanAbsoluteError

Computes the mean absolute error between the labels and predictions.

Inherits From: MeanMetricWrapper, Mean, Metric, Layer, Module

Used in the notebooks

Used in the guide Used in the tutorials

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Standalone usage:

m = tf.keras.metrics.MeanAbsoluteError()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
m.result().numpy()
0.25
m.reset_state()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
               sample_weight=[1, 0])
m.result().numpy()
0.5

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.MeanAbsoluteError()])

Methods

reset_state

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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.

result

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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.

update_state

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Accumulates metric statistics.

For sparse categorical metrics, the shapes of y_true and y_pred are different.

Args
y_true Ground truth label values. shape = [batch_size, d0, .. dN-1] or shape = [batch_size, d0, .. dN-1, 1].
y_pred The predicted probability values. shape = [batch_size, d0, .. dN]