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Class TransformerEncoderImpl#

Inheritance Relationships#

Base Type#

Class Documentation#

class TransformerEncoderImpl : public torch::nn::Cloneable<TransformerEncoderImpl>#

TransformerEncoder module.

See https://pytorch.org/docs/main/generated/torch.nn.TransformerEncoder.html to learn abouut the exact behavior of this encoder layer module.

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

Example:

TransformerEncoderLayer encoderLayer(TransformerEncoderLayerOptions(512,
8).dropout(0.1)); TransformerEncoder
encoder(TransformerEncoderOptions(encoderLayer,
6).norm(LayerNorm(LayerNormOptions({2}))));

Public Functions

inline TransformerEncoderImpl(TransformerEncoderLayer encoder_layer, int64_t num_layers)#
explicit TransformerEncoderImpl(TransformerEncoderOptions options_)#
Tensor forward(const Tensor &src, const Tensor &src_mask = {}, const Tensor &src_key_padding_mask = {})#
virtual void reset() override#

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

void reset_parameters()#

Public Members

TransformerEncoderOptions options#

options with which this TransformerEncoder was constructed

ModuleList layers = nullptr#

module list that contains all the encoder layers

AnyModule norm#

optional normalization module

Protected Functions

inline virtual bool _forward_has_default_args() override#

The following three functions allow a module with default arguments in its forward method to be used in a Sequential module.

You should NEVER override these functions manually. Instead, you should use the FORWARD_HAS_DEFAULT_ARGS macro.

inline virtual unsigned int _forward_num_required_args() override#
inline std::vector<torch::nn::AnyValue> _forward_populate_default_args(std::vector<torch::nn::AnyValue> &&arguments) override#

Friends

friend struct torch::nn::AnyModuleHolder