View source on GitHub |
Computes the categorical hinge loss between y_true
& y_pred
.
Inherits From: Loss
tf.keras.losses.CategoricalHinge(
reduction=losses_utils.ReductionV2.AUTO, name='categorical_hinge'
)
loss = maximum(neg - pos + 1, 0)
where neg=maximum((1-y_true)*y_pred) and pos=sum(y_true*y_pred)
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.CategoricalHinge()
h(y_true, y_pred).numpy()
1.4
# Calling with 'sample_weight'.
h(y_true, y_pred, sample_weight=[1, 0]).numpy()
0.6
# Using 'sum' reduction type.
h = tf.keras.losses.CategoricalHinge(
reduction=tf.keras.losses.Reduction.SUM)
h(y_true, y_pred).numpy()
2.8
# Using 'none' reduction type.
h = tf.keras.losses.CategoricalHinge(
reduction=tf.keras.losses.Reduction.NONE)
h(y_true, y_pred).numpy()
array([1.2, 1.6], dtype=float32)
Usage with the compile()
API:
model.compile(optimizer='sgd', loss=tf.keras.losses.CategoricalHinge())
Args | |
---|---|
reduction
|
Type of tf.keras.losses.Reduction to apply to
loss. Default value is AUTO . AUTO indicates that the
reduction ption will be determined by the usage context. For
almost all cases this defaults to SUM_OVER_BATCH_SIZE . When
used under a tf.distribute.Strategy , except via
Model.compile() and Model.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 'categorical_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 keras.losses.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.
|