description: Computes the mean absolute percentage error between y_true and y_pred.

tf.keras.losses.MAPE

Computes the mean absolute percentage error between y_true and y_pred.

tf.keras.losses.MAPE(
    y_true, y_pred
)

loss = 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)

Standalone usage:

>>> y_true = np.random.random(size=(2, 3))
>>> y_true = np.maximum(y_true, 1e-7)  # Prevent division by zero
>>> y_pred = np.random.random(size=(2, 3))
>>> loss = tf.keras.losses.mean_absolute_percentage_error(y_true, y_pred)
>>> assert loss.shape == (2,)
>>> assert np.array_equal(
...     loss.numpy(),
...     100. * np.mean(np.abs((y_true - y_pred) / y_true), axis=-1))

Args

y_true Ground truth values. shape = [batch_size, d0, .. dN].
y_pred The predicted values. shape = [batch_size, d0, .. dN].

Returns

Mean absolute percentage error values. shape = [batch_size, d0, .. dN-1].