tfa.losses.TripletSemiHardLoss

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Class TripletSemiHardLoss

Computes the triplet loss with semi-hard negative mining.

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Main aliases

tfa.losses.triplet.TripletSemiHardLoss

The loss encourages the positive distances (between a pair of embeddings with the same labels) to be smaller than the minimum negative distance among which are at least greater than the positive distance plus the margin constant (called semi-hard negative) in the mini-batch. If no such negative exists, uses the largest negative distance instead. See: https://arxiv.org/abs/1503.03832.

We expect labels y_true to be provided as 1-D integer Tensor with shape [batch_size] of multi-class integer labels. And embeddings y_pred must be 2-D float Tensor of l2 normalized embedding vectors.

Args:

• margin: Float, margin term in the loss definition. Default value is 1.0.
• name: Optional name for the op.

__init__

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__init__(
    margin=1.0,
    name=None,
    **kwargs
)

Initialize self. See help(type(self)) for accurate signature.

Methods

__call__

__call__(
    y_true,
    y_pred,
    sample_weight=None
)

Invokes the Loss instance.

Args:

• y_true: Ground truth values. shape = [batch_size, d0, .. dN]
• 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.

from_config

@classmethod
from_config(
    cls,
    config
)

Instantiates a Loss from its config (output of get_config()).

Args:

• config: Output of get_config().

Returns:

A Loss instance.

get_config

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get_config()