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Computes the npairs loss between y_true
and y_pred
.
tfa.losses.NpairsLoss(
name: str = 'npairs_loss'
)
Npairs loss expects paired data where a pair is composed of samples from
the same labels and each pairs in the minibatch have different labels.
The loss takes each row of the pair-wise similarity matrix, y_pred
,
as logits and the remapped multi-class labels, y_true
, as labels.
The similarity matrix y_pred
between two embedding matrices a
and b
with shape [batch_size, hidden_size]
can be computed as follows:
a = tf.constant([[1, 2],
[3, 4],
[5, 6]], dtype=tf.float16)
b = tf.constant([[5, 9],
[3, 6],
[1, 8]], dtype=tf.float16)
y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
y_pred
<tf.Tensor: shape=(3, 3), dtype=float16, numpy=
array([[23., 15., 17.],
[51., 33., 35.],
[79., 51., 53.]], dtype=float16)>
<... Note: constants a & b have been used purely for example purposes and have no significant value ...>
Args | |
---|---|
name
|
(Optional) name for the 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.
|