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Struct EmbeddingFuncOptions

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Struct Documentation

struct torch::nn::functional::EmbeddingFuncOptions

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

Example:

namespace F = torch::nn::functional;
F::embedding(input, weight, F::EmbeddingFuncOptions().norm_type(2.5).scale_grad_by_freq(true).sparse(true));

Public Functions

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”.

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

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

auto norm_type(double &&new_norm_type) -> decltype(*this)
const double &norm_type() const noexcept
double &norm_type() noexcept
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.

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

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

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

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