Computes the Huber loss between y_true and y_pred.
tf.keras.losses.Huber(
delta=1.0, reduction=losses_utils.ReductionV2.AUTO, name='huber_loss'
)
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
Usage:
l = tf.keras.losses.Huber()
loss = l([0., 1., 1.], [1., 0., 1.])
print('Loss: ', loss.numpy()) # Loss: 0.333
Usage with the compile API:
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.Huber())
Args |
|---|
delta
|
A float, the point where the Huber loss function changes from a
quadratic to linear.
|
reduction
|
(Optional) Type of tf.keras.losses.Reduction to apply to loss.
Default value is AUTO. AUTO indicates that the reduction option will
be determined by the usage context. For almost all cases this defaults to
SUM_OVER_BATCH_SIZE.
When used with tf.distribute.Strategy, outside of built-in training
loops such as tf.keras compile and fit, using AUTO or
SUM_OVER_BATCH_SIZE will raise an error. Please see
https://www.tensorflow.org/alpha/tutorials/distribute/training_loops
for more details on this.
|
name
|
Optional name for the op.
|
Methods
from_config
View source
@classmethod
from_config(
config
)
Instantiates a Loss from its config (output of get_config()).
| Args |
|---|
config
|
Output of get_config().
|
get_config
View source
get_config()
__call__
View source
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss instance.
| Args |
|---|
y_true
|
Ground truth values. shape = [batch_size, d0, .. dN]
|
y_pred
|
The predicted values. shape = [batch_size, d0, .. dN]
|
sample_weight
|
Optional sample_weight acts as a
coefficient for the loss. If a scalar is provided, then the loss is
simply scaled by the given value. If sample_weight is a tensor of size
[batch_size], then the total loss for each sample of the batch is
rescaled by the corresponding element in the sample_weight vector. If
the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be
broadcasted to this shape), then each loss element of y_pred is scaled
by the corresponding value of sample_weight. (Note ondN-1: all loss
functions reduce by 1 dimension, usually axis=-1.)
|
| Returns |
|---|
Weighted loss float Tensor. If reduction is NONE, this has
shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1
because all loss functions reduce by 1 dimension, usually axis=-1.)
|
| Raises |
|---|
ValueError
|
If the shape of sample_weight is invalid.
|
TensorFlow 1 version
View source on GitHub