Struct EmbeddingFuncOptions

Page Contents

Struct Documentation

struct EmbeddingFuncOptions

Options for torch::nn::functional::embedding.


namespace F = torch::nn::functional;
F::embedding(input, weight,

Public Functions

inline auto padding_idx(const c10::optional<int64_t> &new_padding_idx) -> decltype(*this)

If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e.

it remains as a fixed “pad”.

inline auto padding_idx(c10::optional<int64_t> &&new_padding_idx) -> decltype(*this)
inline const c10::optional<int64_t> &padding_idx() const noexcept
inline c10::optional<int64_t> &padding_idx() noexcept
inline auto max_norm(const c10::optional<double> &new_max_norm) -> decltype(*this)

If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.

inline auto max_norm(c10::optional<double> &&new_max_norm) -> decltype(*this)
inline const c10::optional<double> &max_norm() const noexcept
inline c10::optional<double> &max_norm() noexcept
inline auto norm_type(const double &new_norm_type) -> decltype(*this)

The p of the p-norm to compute for the max_norm option. Default 2.

inline auto norm_type(double &&new_norm_type) -> decltype(*this)
inline const double &norm_type() const noexcept
inline double &norm_type() noexcept
inline auto scale_grad_by_freq(const bool &new_scale_grad_by_freq) -> decltype(*this)

If given, this will scale gradients by the inverse of frequency of the words in the mini-batch.

Default false.

inline auto scale_grad_by_freq(bool &&new_scale_grad_by_freq) -> decltype(*this)
inline const bool &scale_grad_by_freq() const noexcept
inline bool &scale_grad_by_freq() noexcept
inline auto sparse(const bool &new_sparse) -> decltype(*this)

If true, gradient w.r.t. weight matrix will be a sparse tensor.

inline auto sparse(bool &&new_sparse) -> decltype(*this)
inline const bool &sparse() const noexcept
inline bool &sparse() noexcept


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