tf.keras.losses.MAPE

TensorFlow 1 version View source on GitHub

Computes the mean absolute percentage error between y_true and y_pred.

Main aliases

tf.keras.losses.mape, tf.keras.losses.mean_absolute_percentage_error, tf.keras.metrics.MAPE, tf.keras.metrics.mape, tf.keras.metrics.mean_absolute_percentage_error, tf.losses.MAPE, tf.losses.mape, tf.losses.mean_absolute_percentage_error, tf.metrics.MAPE, tf.metrics.mape, tf.metrics.mean_absolute_percentage_error

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.losses.MAPE, tf.compat.v1.keras.losses.mape, tf.compat.v1.keras.losses.mean_absolute_percentage_error, tf.compat.v1.keras.metrics.MAPE, tf.compat.v1.keras.metrics.mape, tf.compat.v1.keras.metrics.mean_absolute_percentage_error

 
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))

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

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