tf.keras.losses.huber

Computes Huber loss value.

tf.keras.losses.huber( y_true, y_pred, delta=1.0 )

Formula:

for x in error: if abs(x) <= delta: loss.append(0.5 * x^2) elif abs(x) > delta: loss.append(delta * abs(x) - 0.5 * delta^2) loss = mean(loss, axis=-1)

See: Huber loss.

Example:

y_true = [[0, 1], [0, 0]] y_pred = [[0.6, 0.4], [0.4, 0.6]] loss = keras.losses.huber(y_true, y_pred) 0.155

y_true tensor of true targets.
y_pred tensor of predicted targets.
delta A float, the point where the Huber loss function changes from a quadratic to linear. Defaults to 1.0.

Tensor with one scalar loss entry per sample.