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Computes the contrastive loss between y_true
and y_pred
.
Aliases:
tfa.losses.contrastive_loss(
y_true,
y_pred,
margin=1.0
)
This loss encourages the embedding to be close to each other for the samples of the same label and the embedding to be far apart at least by the margin constant for the samples of different labels.
The euclidean distances y_pred
between two embedding matrices
a
and b
with shape [batch_size, hidden_size] can be computed
as follows:
# y_pred = \sqrt (\sum_i (a[:, i] - b[:, i])^2)
y_pred = tf.linalg.norm(a - b, axis=1)
See: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
Args:
y_true
: 1-D integerTensor
with shape [batch_size] of binary labels indicating positive vs negative pair.y_pred
: 1-D floatTensor
with shape [batch_size] of distances between two embedding matrices.margin
: margin term in the loss definition.
Returns:
contrastive_loss
: 1-D floatTensor
with shape [batch_size].