View source on GitHub |
Computes the Huber loss between y_true
and y_pred
.
Inherits From: Loss
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
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.
h = tf.keras.losses.Huber(
reduction=tf.keras.losses.Reduction.NONE)
h(y_true, y_pred).numpy()
array([0.18, 0.13], dtype=float32)
Usage with the compile()
API:
model.compile(optimizer='sgd', loss=tf.keras.losses.Huber())
Args | |
---|---|
delta
|
A float, the point where the Huber loss function changes from a quadratic to linear. |
reduction
|
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 this custom training tutorial for
more details.
|
name
|
Optional name for the instance. Defaults to 'huber_loss'. |
Methods
from_config
@classmethod
from_config( config )
Instantiates a Loss
from its config (output of get_config()
).
Args | |
---|---|
config
|
Output of get_config() .
|
Returns | |
---|---|
A Loss instance.
|
get_config
get_config()
Returns the config dictionary for a Loss
instance.
__call__
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss
instance.
Args | |
---|---|
y_true
|
Ground truth values. shape = [batch_size, d0, .. dN] , except
sparse loss functions such as sparse categorical crossentropy where
shape = [batch_size, d0, .. dN-1]
|
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.
|