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tf.keras.losses.MAPE

TensorFlow 1 version View source on GitHub

Computes the mean absolute percentage error between y_true and y_pred.

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

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].