TensorFlow 1 version | View source on GitHub |
Computes the hinge loss between y_true
and y_pred
.
tf.keras.losses.Hinge(
reduction=losses_utils.ReductionV2.AUTO, name='hinge'
)
loss = maximum(1 - y_true * y_pred, 0)
y_true
values are expected to be -1 or 1. If binary (0 or 1) labels are
provided we will convert them to -1 or 1.
Usage:
h = tf.keras.losses.Hinge()
loss = h([-1., 1., 1.], [0.6, -0.7, -0.5])
# loss = max(0, 1 - y_true * y_pred) = [1.6 + 1.7 + 1.5] / 3
print('Loss: ', loss.numpy()) # Loss: 1.6
Usage with the compile
API:
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.Hinge())
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()
__call__
__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.
|