Loss Functions

Loss functions measure how well the model’s predictions match the targets. The choice of loss function depends on your task type and data characteristics.

Regression losses:

• L1Loss/MSELoss: Basic regression losses (MAE vs MSE)

• SmoothL1Loss/HuberLoss: Robust to outliers

Classification losses:

• CrossEntropyLoss: Multi-class classification (combines LogSoftmax + NLLLoss)

• NLLLoss: Negative log likelihood (use with LogSoftmax output)

• BCELoss/BCEWithLogitsLoss: Binary classification

Specialized losses:

• CTCLoss: Sequence-to-sequence without alignment (speech recognition)

• TripletMarginLoss: Metric learning (similarity/embedding tasks)

• CosineEmbeddingLoss: Similarity learning with cosine distance

L1Loss

class L1Loss : public torch::nn::ModuleHolder<L1LossImpl>

A ModuleHolder subclass for L1LossImpl.

See the documentation for L1LossImpl class to learn what methods it provides, and examples of how to use L1Loss with torch::nn::L1LossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = L1LossImpl

struct L1LossImpl : public torch::nn::Cloneable<L1LossImpl>

Creates a criterion that measures the mean absolute error (MAE) between each element in the input : math :x and target : y.

See https://pytorch.org/docs/main/nn.html#torch.nn.L1Loss to learn about the exact behavior of this module.

See the documentation for torch::nn::L1LossOptions class to learn what constructor arguments are supported for this module.

Example:

L1Loss model(L1LossOptions(torch::kNone));

Public Functions

explicit L1LossImpl(L1LossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the L1Loss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

L1LossOptions options

The options with which this Module was constructed.

MSELoss

class MSELoss : public torch::nn::ModuleHolder<MSELossImpl>

A ModuleHolder subclass for MSELossImpl.

See the documentation for MSELossImpl class to learn what methods it provides, and examples of how to use MSELoss with torch::nn::MSELossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = MSELossImpl

struct MSELossImpl : public torch::nn::Cloneable<MSELossImpl>

Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input :math:x and target :math:y.

See https://pytorch.org/docs/main/nn.html#torch.nn.MSELoss to learn about the exact behavior of this module.

See the documentation for torch::nn::MSELossOptions class to learn what constructor arguments are supported for this module.

Example:

MSELoss model(MSELossOptions(torch::kNone));

Public Functions

explicit MSELossImpl(MSELossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the MSELoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

MSELossOptions options

The options with which this Module was constructed.

Example:

auto loss_fn = torch::nn::MSELoss();
auto loss = loss_fn->forward(predictions, targets);

CrossEntropyLoss

class CrossEntropyLoss : public torch::nn::ModuleHolder<CrossEntropyLossImpl>

A ModuleHolder subclass for CrossEntropyLossImpl.

See the documentation for CrossEntropyLossImpl class to learn what methods it provides, and examples of how to use CrossEntropyLoss with torch::nn::CrossEntropyLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = CrossEntropyLossImpl

struct CrossEntropyLossImpl : public torch::nn::Cloneable<CrossEntropyLossImpl>

Creates a criterion that computes cross entropy loss between input and target.

See https://pytorch.org/docs/main/nn.html#torch.nn.CrossEntropyLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::CrossEntropyLossOptions class to learn what constructor arguments are supported for this module.

Example:

CrossEntropyLoss
model(CrossEntropyLossOptions().ignore_index(-100).reduction(torch::kMean));

Public Functions

explicit CrossEntropyLossImpl(CrossEntropyLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the CrossEntropyLoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

CrossEntropyLossOptions options

The options with which this Module was constructed.

Tensor weight

A manual rescaling weight given to each class.

Example:

auto loss_fn = torch::nn::CrossEntropyLoss();
auto logits = torch::randn({32, 10});  // [batch, num_classes]
auto targets = torch::randint(0, 10, {32});  // [batch]
auto loss = loss_fn->forward(logits, targets);

NLLLoss

class NLLLoss : public torch::nn::ModuleHolder<NLLLossImpl>

A ModuleHolder subclass for NLLLossImpl.

See the documentation for NLLLossImpl class to learn what methods it provides, and examples of how to use NLLLoss with torch::nn::NLLLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = NLLLossImpl

struct NLLLossImpl : public torch::nn::Cloneable<NLLLossImpl>

The negative log likelihood loss.

It is useful to train a classification problem with C classes. See https://pytorch.org/docs/main/nn.html#torch.nn.NLLLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::NLLLossOptions class to learn what constructor arguments are supported for this module.

Example:

NLLLoss model(NLLLossOptions().ignore_index(-100).reduction(torch::kMean));

Public Functions

explicit NLLLossImpl(NLLLossOptions options_ = {})

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the NLLLoss module into the given stream.

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

NLLLossOptions options

The options with which this Module was constructed.

Tensor weight

A manual rescaling weight given to each class.

BCELoss

class BCELoss : public torch::nn::ModuleHolder<BCELossImpl>

A ModuleHolder subclass for BCELossImpl.

See the documentation for BCELossImpl class to learn what methods it provides, and examples of how to use BCELoss with torch::nn::BCELossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = BCELossImpl

struct BCELossImpl : public torch::nn::Cloneable<BCELossImpl>

Creates a criterion that measures the Binary Cross Entropy between the target and the output.

See https://pytorch.org/docs/main/nn.html#torch.nn.BCELoss to learn about the exact behavior of this module.

See the documentation for torch::nn::BCELossOptions class to learn what constructor arguments are supported for this module.

Example:

BCELoss model(BCELossOptions().reduction(torch::kNone).weight(weight));

Public Functions

explicit BCELossImpl(BCELossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the BCELoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

BCELossOptions options

The options with which this Module was constructed.

BCEWithLogitsLoss

class BCEWithLogitsLoss : public torch::nn::ModuleHolder<BCEWithLogitsLossImpl>

A ModuleHolder subclass for BCEWithLogitsLossImpl.

See the documentation for BCEWithLogitsLossImpl class to learn what methods it provides, and examples of how to use BCEWithLogitsLoss with torch::nn::BCEWithLogitsLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = BCEWithLogitsLossImpl

struct BCEWithLogitsLossImpl : public torch::nn::Cloneable<BCEWithLogitsLossImpl>

This loss combines a Sigmoid layer and the BCELoss in one single class.

This version is more numerically stable than using a plain Sigmoid followed by a BCELoss as, by combining the operations into one layer, we take advantage of the log-sum-exp trick for numerical stability. See https://pytorch.org/docs/main/nn.html#torch.nn.BCEWithLogitsLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::BCEWithLogitsLossOptions class to learn what constructor arguments are supported for this module.

Example:

BCEWithLogitsLoss
model(BCEWithLogitsLossOptions().reduction(torch::kNone).weight(weight));

Public Functions

explicit BCEWithLogitsLossImpl(BCEWithLogitsLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the BCEWithLogitsLoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

BCEWithLogitsLossOptions options

The options with which this Module was constructed.

Tensor weight

A manual rescaling weight given to the loss of each batch element.

Tensor pos_weight

A weight of positive examples.

HuberLoss

class HuberLoss : public torch::nn::ModuleHolder<HuberLossImpl>

A ModuleHolder subclass for HuberLossImpl.

See the documentation for HuberLossImpl class to learn what methods it provides, and examples of how to use HuberLoss with torch::nn::HuberLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = HuberLossImpl

struct HuberLossImpl : public torch::nn::Cloneable<HuberLossImpl>

Creates a criterion that uses a squared term if the absolute element-wise error falls below delta and a delta-scaled L1 term otherwise.

See https://pytorch.org/docs/main/nn.html#torch.nn.HuberLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::HuberLossOptions class to learn what constructor arguments are supported for this module.

Example:

HuberLoss model(HuberLossOptions().reduction(torch::kNone).delta(0.5));

Public Functions

explicit HuberLossImpl(HuberLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the HuberLoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

HuberLossOptions options

The options with which this Module was constructed.

SmoothL1Loss

class SmoothL1Loss : public torch::nn::ModuleHolder<SmoothL1LossImpl>

A ModuleHolder subclass for SmoothL1LossImpl.

See the documentation for SmoothL1LossImpl class to learn what methods it provides, and examples of how to use SmoothL1Loss with torch::nn::SmoothL1LossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = SmoothL1LossImpl

struct SmoothL1LossImpl : public torch::nn::Cloneable<SmoothL1LossImpl>

Creates a criterion that uses a squared term if the absolute element-wise error falls below beta and an L1 term otherwise.

It is less sensitive to outliers than the MSELoss and in some cases prevents exploding gradients (e.g. see the paper Fast R-CNN by Ross Girshick). See https://pytorch.org/docs/main/nn.html#torch.nn.SmoothL1Loss to learn about the exact behavior of this module.

See the documentation for torch::nn::SmoothL1LossOptions class to learn what constructor arguments are supported for this module.

Example:

SmoothL1Loss model(SmoothL1LossOptions().reduction(torch::kNone).beta(0.5));

Public Functions

explicit SmoothL1LossImpl(SmoothL1LossOptions options = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the L1Loss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

SmoothL1LossOptions options

The options with which this Module was constructed.

KLDivLoss

class KLDivLoss : public torch::nn::ModuleHolder<KLDivLossImpl>

A ModuleHolder subclass for KLDivLossImpl.

See the documentation for KLDivLossImpl class to learn what methods it provides, and examples of how to use KLDivLoss with torch::nn::KLDivLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = KLDivLossImpl

struct KLDivLossImpl : public torch::nn::Cloneable<KLDivLossImpl>

The Kullback-Leibler divergence loss measure See https://pytorch.org/docs/main/nn.html#torch.nn.KLDivLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::KLDivLossOptions class to learn what constructor arguments are supported for this module.

Example:

KLDivLoss model(KLDivLossOptions().reduction(torch::kNone));

Public Functions

explicit KLDivLossImpl(KLDivLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the KLDivLoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

KLDivLossOptions options

The options with which this Module was constructed.

CTCLoss

class CTCLoss : public torch::nn::ModuleHolder<CTCLossImpl>

A ModuleHolder subclass for CTCLossImpl.

See the documentation for CTCLossImpl class to learn what methods it provides, and examples of how to use CTCLoss with torch::nn::CTCLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = CTCLossImpl

struct CTCLossImpl : public torch::nn::Cloneable<CTCLossImpl>

The Connectionist Temporal Classification loss.

See https://pytorch.org/docs/main/nn.html#torch.nn.CTCLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::CTCLossOptions class to learn what constructor arguments are supported for this module.

Example:

CTCLoss
model(CTCLossOptions().blank(42).zero_infinity(false).reduction(torch::kSum));

Public Functions

explicit CTCLossImpl(CTCLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the CTCLoss module into the given stream.

Tensor forward(const Tensor &log_probs, const Tensor &targets, const Tensor &input_lengths, const Tensor &target_lengths)

Public Members

CTCLossOptions options

The options with which this Module was constructed.

PoissonNLLLoss

class PoissonNLLLoss : public torch::nn::ModuleHolder<PoissonNLLLossImpl>

A ModuleHolder subclass for PoissonNLLLossImpl.

See the documentation for PoissonNLLLossImpl class to learn what methods it provides, and examples of how to use PoissonNLLLoss with torch::nn::PoissonNLLLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = PoissonNLLLossImpl

struct PoissonNLLLossImpl : public torch::nn::Cloneable<PoissonNLLLossImpl>

Negative log likelihood loss with Poisson distribution of target.

See https://pytorch.org/docs/main/nn.html#torch.nn.PoissonNLLLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::PoissonNLLLossOptions class to learn what constructor arguments are supported for this module.

Example:

PoissonNLLLoss
model(PoissonNLLLossOptions().log_input(false).full(true).eps(0.42).reduction(torch::kSum));

Public Functions

explicit PoissonNLLLossImpl(PoissonNLLLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the PoissonNLLLoss module into the given stream.

Tensor forward(const Tensor &log_input, const Tensor &targets)

Public Members

PoissonNLLLossOptions options

The options with which this Module was constructed.

MarginRankingLoss

class MarginRankingLoss : public torch::nn::ModuleHolder<MarginRankingLossImpl>

A ModuleHolder subclass for MarginRankingLossImpl.

See the documentation for MarginRankingLossImpl class to learn what methods it provides, and examples of how to use MarginRankingLoss with torch::nn::MarginRankingLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = MarginRankingLossImpl

struct MarginRankingLossImpl : public torch::nn::Cloneable<MarginRankingLossImpl>

Creates a criterion that measures the loss given inputs :math:x1, :math:x2, two 1D mini-batch Tensors, and a label 1D mini-batch tensor :math:y (containing 1 or -1).

See https://pytorch.org/docs/main/nn.html#torch.nn.MarginRankingLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::MarginRankingLossOptions class to learn what constructor arguments are supported for this module.

Example:

MarginRankingLoss
model(MarginRankingLossOptions().margin(0.5).reduction(torch::kSum));

Public Functions

explicit MarginRankingLossImpl(MarginRankingLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the MarginRankingLoss module into the given stream.

Tensor forward(const Tensor &input1, const Tensor &input2, const Tensor &targets)

Public Members

MarginRankingLossOptions options

The options with which this Module was constructed.

HingeEmbeddingLoss

class HingeEmbeddingLoss : public torch::nn::ModuleHolder<HingeEmbeddingLossImpl>

A ModuleHolder subclass for HingeEmbeddingLossImpl.

See the documentation for HingeEmbeddingLossImpl class to learn what methods it provides, and examples of how to use HingeEmbeddingLoss with torch::nn::HingeEmbeddingLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = HingeEmbeddingLossImpl

struct HingeEmbeddingLossImpl : public torch::nn::Cloneable<HingeEmbeddingLossImpl>

Creates a criterion that measures the loss given an input tensor :math:x and a labels tensor :math:y (containing 1 or -1).

See https://pytorch.org/docs/main/nn.html#torch.nn.HingeEmbeddingLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::HingeEmbeddingLossOptions class to learn what constructor arguments are supported for this module.

Example:

HingeEmbeddingLoss
model(HingeEmbeddingLossOptions().margin(4).reduction(torch::kNone));

Public Functions

explicit HingeEmbeddingLossImpl(HingeEmbeddingLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the HingeEmbeddingLoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

HingeEmbeddingLossOptions options

The options with which this Module was constructed.

CosineEmbeddingLoss

class CosineEmbeddingLoss : public torch::nn::ModuleHolder<CosineEmbeddingLossImpl>

A ModuleHolder subclass for CosineEmbeddingLossImpl.

See the documentation for CosineEmbeddingLossImpl class to learn what methods it provides, and examples of how to use CosineEmbeddingLoss with torch::nn::CosineEmbeddingLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = CosineEmbeddingLossImpl

struct CosineEmbeddingLossImpl : public torch::nn::Cloneable<CosineEmbeddingLossImpl>

Creates a criterion that measures the loss given input tensors input1, input2, and a Tensor label target with values 1 or -1.

This is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning. See https://pytorch.org/docs/main/nn.html#torch.nn.CosineEmbeddingLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::CosineEmbeddingLossOptions class to learn what constructor arguments are supported for this module.

Example:

CosineEmbeddingLoss model(CosineEmbeddingLossOptions().margin(0.5));

Public Functions

explicit CosineEmbeddingLossImpl(CosineEmbeddingLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the CosineEmbeddingLoss module into the given stream.

Tensor forward(const Tensor &input1, const Tensor &input2, const Tensor &target)

Public Members

CosineEmbeddingLossOptions options

The options with which this Module was constructed.

MultiMarginLoss

class MultiMarginLoss : public torch::nn::ModuleHolder<MultiMarginLossImpl>

A ModuleHolder subclass for MultiMarginLossImpl.

See the documentation for MultiMarginLossImpl class to learn what methods it provides, and examples of how to use MultiMarginLoss with torch::nn::MultiMarginLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = MultiMarginLossImpl

struct MultiMarginLossImpl : public torch::nn::Cloneable<MultiMarginLossImpl>

Creates a criterion that optimizes a multi-class classification hinge loss (margin-based loss) between input :math:x (a 2D mini-batch Tensor) and output :math:y (which is a 1D tensor of target class indices, :math:0 \leq y \leq \text{x.size}(1)-1).

See https://pytorch.org/docs/main/nn.html#torch.nn.MultiMarginLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::MultiMarginLossOptions class to learn what constructor arguments are supported for this module.

Example:

MultiMarginLoss model(MultiMarginLossOptions().margin(2).weight(weight));

Public Functions

explicit MultiMarginLossImpl(MultiMarginLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the MultiMarginLoss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

MultiMarginLossOptions options

The options with which this Module was constructed.

MultiLabelMarginLoss

class MultiLabelMarginLoss : public torch::nn::ModuleHolder<MultiLabelMarginLossImpl>

A ModuleHolder subclass for MultiLabelMarginLossImpl.

See the documentation for MultiLabelMarginLossImpl class to learn what methods it provides, and examples of how to use MultiLabelMarginLoss with torch::nn::MultiLabelMarginLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = MultiLabelMarginLossImpl

struct MultiLabelMarginLossImpl : public torch::nn::Cloneable<MultiLabelMarginLossImpl>

Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input :math:x (a 2D mini-batch Tensor) and output :math:y (which is a 2D Tensor of target class indices).

See https://pytorch.org/docs/main/nn.html#torch.nn.MultiLabelMarginLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::MultiLabelMarginLossOptions class to learn what constructor arguments are supported for this module.

Example:

MultiLabelMarginLoss model(MultiLabelMarginLossOptions(torch::kNone));

Public Functions

explicit MultiLabelMarginLossImpl(MultiLabelMarginLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the L1Loss module into the given stream.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

MultiLabelMarginLossOptions options

The options with which this Module was constructed.

MultiLabelSoftMarginLoss

class MultiLabelSoftMarginLoss : public torch::nn::ModuleHolder<MultiLabelSoftMarginLossImpl>

A ModuleHolder subclass for MultiLabelSoftMarginLossImpl.

See the documentation for MultiLabelSoftMarginLossImpl class to learn what methods it provides, and examples of how to use MultiLabelSoftMarginLoss with torch::nn::MultiLabelSoftMarginLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = MultiLabelSoftMarginLossImpl

struct MultiLabelSoftMarginLossImpl : public torch::nn::Cloneable<MultiLabelSoftMarginLossImpl>

Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input :math:x and target :math:y of size :math:(N, C).

See https://pytorch.org/docs/main/nn.html#torch.nn.MultiLabelSoftMarginLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::MultiLabelSoftMarginLossOptions class to learn what constructor arguments are supported for this module.

Example:

MultiLabelSoftMarginLoss
model(MultiLabelSoftMarginLossOptions().reduction(torch::kNone).weight(weight));

Public Functions

explicit MultiLabelSoftMarginLossImpl(MultiLabelSoftMarginLossOptions options_ = {})

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the MultiLabelSoftMarginLoss module into the given stream.

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

MultiLabelSoftMarginLossOptions options

The options with which this Module was constructed.

SoftMarginLoss

class SoftMarginLoss : public torch::nn::ModuleHolder<SoftMarginLossImpl>

A ModuleHolder subclass for SoftMarginLossImpl.

See the documentation for SoftMarginLossImpl class to learn what methods it provides, and examples of how to use SoftMarginLoss with torch::nn::SoftMarginLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = SoftMarginLossImpl

struct SoftMarginLossImpl : public torch::nn::Cloneable<SoftMarginLossImpl>

Creates a criterion that optimizes a two-class classification logistic loss between input tensor :math:x and target tensor :math:y (containing 1 or -1).

See https://pytorch.org/docs/main/nn.html#torch.nn.SoftMarginLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::SoftMarginLossOptions class to learn what constructor arguments are supported for this module.

Example:

SoftMarginLoss model(SoftMarginLossOptions(torch::kNone));

Public Functions

explicit SoftMarginLossImpl(SoftMarginLossOptions options_ = {})

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the SoftMarginLoss module into the given stream.

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

Tensor forward(const Tensor &input, const Tensor &target)

Public Members

SoftMarginLossOptions options

The options with which this Module was constructed.

TripletMarginLoss

class TripletMarginLoss : public torch::nn::ModuleHolder<TripletMarginLossImpl>

A ModuleHolder subclass for TripletMarginLossImpl.

See the documentation for TripletMarginLossImpl class to learn what methods it provides, and examples of how to use TripletMarginLoss with torch::nn::TripletMarginLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = TripletMarginLossImpl

struct TripletMarginLossImpl : public torch::nn::Cloneable<TripletMarginLossImpl>

Creates a criterion that measures the triplet loss given an input tensors :math:x1, :math:x2, :math:x3 and a margin with a value greater than :math:0.

This is used for measuring a relative similarity between samples. A triplet is composed by a, p and n (i.e., anchor, positive examples and negative examples respectively). The shapes of all input tensors should be :math:(N, D). See https://pytorch.org/docs/main/nn.html#torch.nn.TripletMarginLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::TripletMarginLossOptions class to learn what constructor arguments are supported for this module.

Example:

TripletMarginLoss
model(TripletMarginLossOptions().margin(3).p(2).eps(1e-06).swap(false));

Public Functions

explicit TripletMarginLossImpl(TripletMarginLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the TripletMarginLoss module into the given stream.

Tensor forward(const Tensor &anchor, const Tensor &positive, const Tensor &negative)

Public Members

TripletMarginLossOptions options

The options with which this Module was constructed.

TripletMarginWithDistanceLoss

class TripletMarginWithDistanceLoss : public torch::nn::ModuleHolder<TripletMarginWithDistanceLossImpl>

A ModuleHolder subclass for TripletMarginWithDistanceLossImpl.

See the documentation for TripletMarginWithDistanceLossImpl class to learn what methods it provides, and examples of how to use TripletMarginWithDistanceLoss with torch::nn::TripletMarginWithDistanceLossOptions. See the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = TripletMarginWithDistanceLossImpl

struct TripletMarginWithDistanceLossImpl : public torch::nn::Cloneable<TripletMarginWithDistanceLossImpl>

Creates a criterion that measures the triplet loss given input tensors :math:a, :math:p, and :math:n (representing anchor, positive, and negative examples, respectively); and a nonnegative, real-valued function (“distance function”) used to compute the relationships between the anchor and positive example (“positive distance”) and the anchor and negative example (“negative distance”).

See https://pytorch.org/docs/main/nn.html#torch.nn.TripletMarginWithDistanceLoss to learn about the exact behavior of this module.

See the documentation for torch::nn::TripletMarginWithDistanceLossOptions class to learn what constructor arguments are supported for this module.

Example:

TripletMarginWithDistanceLoss
model(TripletMarginWithDistanceLossOptions().margin(3).swap(false));

Public Functions

explicit TripletMarginWithDistanceLossImpl(TripletMarginWithDistanceLossOptions options_ = {})

virtual void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

virtual void pretty_print(std::ostream &stream) const override

Pretty prints the TripletMarginWithDistanceLoss module into the given stream.

Tensor forward(const Tensor &anchor, const Tensor &positive, const Tensor &negative)

Public Members

TripletMarginWithDistanceLossOptions options

The options with which this Module was constructed.