Linear Layers

Linear layers apply affine transformations to input data: y = xW^T + b. They are the building blocks of fully-connected networks and are used for feature transformation, classification heads, and projection layers.

• Linear: Standard fully-connected layer

• Bilinear: Bilinear transformation of two inputs

• Identity: Pass-through layer (useful for skip connections)

• Flatten/Unflatten: Reshape tensors between convolutional and linear layers

Linear

class Linear : public torch::nn::ModuleHolder<LinearImpl>

A ModuleHolder subclass for LinearImpl.

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

Public Types

using Impl = LinearImpl

class LinearImpl : public torch::nn::Cloneable<LinearImpl>

Applies a linear transformation with optional bias.

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

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

Example:

Linear model(LinearOptions(5, 2).bias(false));

Public Functions

inline LinearImpl(int64_t in_features, int64_t out_features)

explicit LinearImpl(const LinearOptions &options_)

virtual void reset() override

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

void reset_parameters()

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

Pretty prints the Linear module into the given stream.

Tensor forward(const Tensor &input)

Transforms the input tensor by multiplying with the weight and optionally adding the bias, if with_bias is true in the options.

Public Members

LinearOptions options

The options used to configure this module.

Tensor weight

The learned weight.

Tensor bias

The learned bias.

If bias is false in the options, this tensor is undefined.

Example:

auto linear = torch::nn::Linear(torch::nn::LinearOptions(784, 256).bias(true));
auto output = linear->forward(input);  // input: [N, 784]

Bilinear

class Bilinear : public torch::nn::ModuleHolder<BilinearImpl>

A ModuleHolder subclass for BilinearImpl.

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

Public Types

using Impl = BilinearImpl

class BilinearImpl : public torch::nn::Cloneable<BilinearImpl>

Applies a billinear transformation with optional bias.

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

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

Example:

Bilinear model(BilinearOptions(3, 2, 4).bias(false));

Public Functions

inline BilinearImpl(int64_t in1_features, int64_t in2_features, int64_t out_features)

explicit BilinearImpl(const BilinearOptions &options_)

virtual void reset() override

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

void reset_parameters()

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

Pretty prints the Bilinear module into the given stream.

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

Applies a bilinear transform on the input1 and input2 tensor by multiplying with the weight and optionally adding the bias, if with_bias is true in the options.

Public Members

BilinearOptions options

The options used to configure this module.

Tensor weight

The learned weight.

Tensor bias

The learned bias.

If with_bias is false in the options, this tensor is undefined.

Identity

class Identity : public torch::nn::ModuleHolder<IdentityImpl>

A ModuleHolder subclass for IdentityImpl.

See the documentation for IdentityImpl class to learn what methods it provides, or the documentation for ModuleHolder to learn about PyTorch’s module storage semantics.

Public Types

using Impl = IdentityImpl

class IdentityImpl : public torch::nn::Cloneable<IdentityImpl>

A placeholder identity operator that is argument-insensitive.

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

Public Functions

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 Identity module into the given stream.

Tensor forward(const Tensor &input)

Flatten

class Flatten : public torch::nn::ModuleHolder<FlattenImpl>

A ModuleHolder subclass for FlattenImpl.

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

Public Types

using Impl = FlattenImpl

class FlattenImpl : public torch::nn::Cloneable<FlattenImpl>

A placeholder for Flatten operator See https://pytorch.org/docs/main/generated/torch.nn.Flatten.html to learn about the exact behavior of this module.

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

Example:

Flatten model(FlattenOptions().start_dim(2).end_dim(4));

Public Functions

explicit FlattenImpl(const FlattenOptions &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 Flatten module into the given stream.

Tensor forward(const Tensor &input)

Applies a flatten transform on the input.

Public Members

FlattenOptions options

The options used to configure this module.

Unflatten

class Unflatten : public torch::nn::ModuleHolder<UnflattenImpl>

A ModuleHolder subclass for UnflattenImpl.

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

Public Types

using Impl = UnflattenImpl

class UnflattenImpl : public torch::nn::Cloneable<UnflattenImpl>

A placeholder for unflatten operator See https://pytorch.org/docs/main/generated/torch.nn.Unflatten.html to learn about the exact behavior of this module.

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

Example:

Unflatten model(UnflattenOptions(0, {2, 2}));
Unflatten model(UnflattenOptions("B", {{"B1", 2}, {"B2", 2}}));

Public Functions

inline UnflattenImpl(int64_t dim, std::vector<int64_t> sizes)

inline UnflattenImpl(std::string dimname, UnflattenOptions::namedshape_t namedshape)

explicit UnflattenImpl(UnflattenOptions 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 Unflatten module into the given stream.

Tensor forward(const Tensor &input)

Applies an unflatten transform on the input.

Public Members

UnflattenOptions options

The options used to configure this module.