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Computes the npairs loss between y_true
and y_pred
.
@tf.function
tfa.losses.npairs_loss( y_true:
tfa.types.TensorLike
, y_pred:tfa.types.TensorLike
) -> tf.Tensor
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 | |
---|---|
y_true
|
1-D integer Tensor with shape [batch_size] of
multi-class labels.
|
y_pred
|
2-D float Tensor with shape [batch_size, batch_size] of
similarity matrix between embedding matrices.
|
Returns | |
---|---|
npairs_loss
|
float scalar. |