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TFSimilarity.losses.PNLoss

Computes the PN loss in an online fashion.

Inherits From: MetricLoss

TFSimilarity.losses.PNLoss(
    positive_mining_strategy: str = hard,
    negative_mining_strategy: str = semi-hard,
    soft_margin: bool = False,
    margin: float = 1.0,
    name: str = PNLoss,
    **kwargs
)

This loss encourages the positive distances between a pair of embeddings with the same labels to be smaller than the minimum negative distances between pair of embeddings of different labels. Additionally, both the anchor and the positive embeddings are encouraged to be far from the negative embeddings. This is accomplished by taking the min(pos_neg_dist, anchor_neg_dist) and using that as the negative distance in the triplet loss.

See PN Loss Ivis:

Szubert, B., Cole, J.E., Monaco, C. et al. Structure-preserving visualisation of high dimensional single-cell dataset Sci Rep 9, 8914 (2019). https://doi.org/10.1038/s41598-019-45301-0

y_true must be a 1-D integer Tensor of shape (batch_size,). It's values represent the classes associated with the examples as integer values.

y_pred must be 2-D float Tensor of L2 normalized embedding vectors. you can use the layer tensorflow_similarity.layers.L2Embedding() as the last layer of your model to ensure your model output is properly normalized.

Args

distance Which distance function to use to compute the pairwise distances between embeddings. Defaults to 'cosine'.
positive_mining_strategy What mining strategy to use to select embedding from the same class. Defaults to 'hard'.
available 'easy', 'hard'
negative_mining_strategy What mining strategy to use for select the embedding from the different class. Defaults to 'semi-hard'. Available: 'hard', 'semi-hard', 'easy'
soft_margin - [description]. Defaults to True. Use a soft margin instead of an explicit one.
margin Use an explicit value for the margin term. Defaults to 1.0.
name Loss name. Defaults to PNLoss.

Raises

ValueError Invalid positive mining strategy.
ValueError Invalid negative mining strategy.
ValueError Margin value is not used when soft_margin is set to True.

Methods

from_config

<b>python @classmethod</b>

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() -> Dict[str, Any]

Contains the loss configuration.

Returns
A Python dict containing the configuration of the loss.

__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.