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].
|
Example:
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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