tf.keras.losses.Huber

Computes the Huber loss between y_true and y_pred.

Used in the notebooks

Used in the tutorials

For each value x in error = y_true - y_pred:

loss = 0.5 * x^2                  if |x| <= d
loss = 0.5 * d^2 + d * (|x| - d)  if |x| > d

where d is delta. See: https://en.wikipedia.org/wiki/Huber_loss

Standalone usage:

y_true = [[0, 1], [0, 0]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
# Using 'auto'/'sum_over_batch_size' reduction type.
h = tf.keras.losses.Huber()
h(y_true, y_pred).numpy()
0.155
# Calling with 'sample_weight'.
h(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.09
# Using 'sum' reduction type.
h = tf.keras.losses.Huber(
    reduction=tf.keras.losses.Reduction.SUM)
h(y_true, y_pred).numpy()
0.31
# Using 'none' reduction type.