Computes the mean absolute percentage error between y_true & y_pred.
tf.keras.losses.MAPE(
y_true, y_pred
)
loss = 100 * mean(abs((y_true - y_pred) / y_true), axis=-1)
Division by zero is prevented by dividing by maximum(y_true, epsilon) where epsilon = keras.backend.epsilon() (default to 1e-7).
| Args | |
|---|---|
y_true | Ground truth values with shape = [batch_size, d0, .. dN]. |
y_pred | The predicted values with shape = [batch_size, d0, .. dN]. |
| Returns | |
|---|---|
Mean absolute percentage error values with shape = [batch_size, d0, .. dN-1]. |
y_true = np.random.random(size=(2, 3)) y_pred = np.random.random(size=(2, 3)) loss = keras.losses.mean_absolute_percentage_error(y_true, y_pred)
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Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAPE