Computes the mean absolute percentage error between y_true and y_pred.
View aliases
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))
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].
|
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TensorFlow 1 version
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