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Sparsemax loss function.
tfa.losses.SparsemaxLoss(
from_logits: bool = True,
reduction: str = tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
name: str = 'sparsemax_loss'
)
Computes the generalized multi-label classification loss for the sparsemax function.
Because the sparsemax loss function needs both the probability output and
the logits to compute the loss value, from_logits
must be True
.
Because it computes the generalized multi-label loss, the shape of both
y_pred
and y_true
must be [batch_size, num_classes]
.
Args | |
---|---|
from_logits
|
Whether y_pred is expected to be a logits tensor. Default
is True , meaning y_pred is the logits.
|
reduction
|
(Optional) Type of tf.keras.losses.Reduction to apply to
loss. Default value is SUM_OVER_BATCH_SIZE .
|
name
|
Optional name for the op. |
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.
|