pytext.loss package

Submodules

pytext.loss.loss module

class pytext.loss.loss.AUCPRHingeLoss(config, weights=None, *args, **kwargs)

Bases: torch.nn.modules.module.Module, pytext.loss.loss.Loss

area under the precision-recall curve loss, Reference: “Scalable Learning of Non-Decomposable Objectives”, Section 5 TensorFlow Implementation: https://github.com/tensorflow/models/tree/master/research/global_objectives

forward(logits, targets, reduce=True, size_average=True, weights=None)

Parameters:

• logits – Variable \((N, C)\) where C = number of classes
• targets – Variable \((N)\) where each value is 0 <= targets[i] <= C-1
• weights – Coefficients for the loss. Must be a Tensor of shape [N] or [N, C], where N = batch_size, C = number of classes.
• size_average (bool, optional) – By default, the losses are averaged over observations for each minibatch. However, if the field sizeAverage is set to False, the losses are instead summed for each minibatch. Default: True
• reduce (bool, optional) – By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per input/target element instead and ignores size_average. Default: True

class pytext.loss.loss.BinaryCrossEntropyLoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.CosineEmbeddingLoss(config, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.CrossEntropyLoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.KLDivergenceBCELoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.KLDivergenceCELoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.LabelSmoothedCrossEntropyLoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.Loss(config=None, *args, **kwargs)

Bases: pytext.config.component.Component

Base class for loss functions

class pytext.loss.loss.MAELoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

Mean absolute error or L1 loss, for regression tasks.

class pytext.loss.loss.MSELoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

Mean squared error or L2 loss, for regression tasks.

class pytext.loss.loss.MultiLabelSoftMarginLoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.NLLLoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.loss.PairwiseRankingLoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

Given embeddings for a query, positive response and negative response computes pairwise ranking hinge loss

static get_similarities(embeddings)

Module contents

class pytext.loss.AUCPRHingeLoss(config, weights=None, *args, **kwargs)

Bases: torch.nn.modules.module.Module, pytext.loss.loss.Loss

area under the precision-recall curve loss, Reference: “Scalable Learning of Non-Decomposable Objectives”, Section 5 TensorFlow Implementation: https://github.com/tensorflow/models/tree/master/research/global_objectives

forward(logits, targets, reduce=True, size_average=True, weights=None)

Parameters:

• logits – Variable \((N, C)\) where C = number of classes
• targets – Variable \((N)\) where each value is 0 <= targets[i] <= C-1
• weights – Coefficients for the loss. Must be a Tensor of shape [N] or [N, C], where N = batch_size, C = number of classes.
• size_average (bool, optional) – By default, the losses are averaged over observations for each minibatch. However, if the field sizeAverage is set to False, the losses are instead summed for each minibatch. Default: True
• reduce (bool, optional) – By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per input/target element instead and ignores size_average. Default: True

class pytext.loss.Loss(config=None, *args, **kwargs)

Bases: pytext.config.component.Component

Base class for loss functions

class pytext.loss.CrossEntropyLoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.CosineEmbeddingLoss(config, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.BinaryCrossEntropyLoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.MultiLabelSoftMarginLoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.KLDivergenceBCELoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.KLDivergenceCELoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.MAELoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

Mean absolute error or L1 loss, for regression tasks.

class pytext.loss.MSELoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

Mean squared error or L2 loss, for regression tasks.

class pytext.loss.NLLLoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

class pytext.loss.PairwiseRankingLoss(config=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss

Given embeddings for a query, positive response and negative response computes pairwise ranking hinge loss

static get_similarities(embeddings)

class pytext.loss.LabelSmoothedCrossEntropyLoss(config, ignore_index=-100, weight=None, *args, **kwargs)

Bases: pytext.loss.loss.Loss