tfa.losses.contrastive_loss

Computes the contrastive loss between y_true and y_pred.

@tf.function
tfa.losses.contrastive_loss(
    y_true: tfa.types.TensorLike,
    y_pred: tfa.types.TensorLike,
    margin: tfa.types.Number = 1.0
) -> tf.Tensor

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:

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.linalg.norm(a - b, axis=1)
y_pred
<tf.Tensor: shape=(3,), dtype=float16, numpy=array([8.06 , 2.   , 4.473],
dtype=float16)>

<... Note: constants a & b have been used purely for example purposes and have no significant value ...>

See: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

Args
`y_true`	1-D integer `Tensor` with shape `[batch_size]` of binary labels indicating positive vs negative pair.
`y_pred`	1-D float `Tensor` with shape `[batch_size]` of distances between two embedding matrices.
`margin`	margin term in the loss definition.

Returns
`contrastive_loss`	1-D float `Tensor` with shape `[batch_size]`.

tfa.losses.contrastive_loss

Args

Returns