tfa.losses.ContrastiveLoss

Computes the contrastive loss between y_true and y_pred.

tfa.losses.ContrastiveLoss(
    margin: tfa.types.Number = 1.0,
    reduction: str = tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
    name: str = 'contrastive_loss'
)

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.

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

We expect labels y_true to be provided as 1-D integer Tensor with shape [batch_size] of binary integer labels. And y_pred must be 1-D float Tensor with shape [batch_size] of distances between two embedding matrices.

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 ...>

margin Float, margin term in the loss definition. Default value is 1.0.
reduction (Optional) Type of tf.keras.losses.Reduction to apply. Default value is SUM_OVER_BATCH_SIZE.
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

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.