Computes the mean absolute error between labels and predictions.
View aliases
Main aliases
tf.keras.losses.MAE
, tf.keras.losses.mae
, tf.keras.losses.mean_absolute_error
, tf.keras.metrics.MAE
, tf.keras.metrics.mae
, tf.losses.MAE
, tf.losses.mae
, tf.losses.mean_absolute_error
, tf.metrics.MAE
, tf.metrics.mae
, tf.metrics.mean_absolute_error
Compat aliases for migration
See
Migration guide for
more details.
`tf.compat.v1.keras.losses.MAE`, `tf.compat.v1.keras.losses.mae`, `tf.compat.v1.keras.losses.mean_absolute_error`, `tf.compat.v1.keras.metrics.MAE`, `tf.compat.v1.keras.metrics.mae`, `tf.compat.v1.keras.metrics.mean_absolute_error`
tf.keras.metrics.mean_absolute_error(
y_true, y_pred
)
loss = mean(abs(y_true - y_pred), axis=-1)
Standalone usage:
y_true = np.random.randint(0, 2, size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.mean_absolute_error(y_true, y_pred)
assert loss.shape == (2,)
assert np.array_equal(
loss.numpy(), np.mean(np.abs(y_true - y_pred), 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 error values. shape = [batch_size, d0, .. dN-1] .
|