Computes the triplet loss with semi-hard negative mining.
tfa.losses.TripletSemiHardLoss(
margin: tfa.types.FloatTensorLike
= 1.0,
distance_metric: Union[str, Callable] = 'L2',
name: Optional[str] = None,
**kwargs
)
Used in the notebooks
The loss encourages the positive distances (between a pair of embeddings
with the same labels) to be smaller than the minimum negative distance
among which are at least greater than the positive distance plus the
margin constant (called semi-hard negative) in the mini-batch.
If no such negative exists, uses the largest negative distance instead.
See: https://arxiv.org/abs/1503.03832
We expect labels y_true
to be provided as 1-D integer Tensor
with shape
[batch_size]
of multi-class integer labels. And embeddings y_pred
must be
2-D float Tensor
of l2 normalized embedding vectors.
Args |
margin
|
Float, margin term in the loss definition. Default value is 1.0.
|
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() .
|
get_config
View source
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.
|