tf.keras.losses.MAE | TensorFlow v2.16.1 (original) (raw)
Computes the mean absolute error between labels and predictions.
View aliases
Main aliases
tf.keras.losses.mae, tf.keras.metrics.MAE, tf.keras.metrics.mae
tf.keras.losses.MAE(
y_true, y_pred
)
Used in the notebooks
| Used in the tutorials |
|---|
| Intro to Autoencoders |
loss = mean(abs(y_true - y_pred), axis=-1)
| 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 error values with shape = [batch_size, d0, .. dN-1]. |
Example:
y_true = np.random.randint(0, 2, size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = keras.losses.mean_absolute_error(y_true, y_pred)
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Last updated 2024-06-07 UTC.