Namespace faiss::nn

namespace nn

Typedefs

using Tensor2D = Tensor2DTemplate<float>

using Int32Tensor2D = Tensor2DTemplate<int32_t>

template<typename T>
struct Tensor2DTemplate

Public Functions

Tensor2DTemplate(size_t n0, size_t n1, const T *data = nullptr)

Tensor2DTemplate &operator+=(const Tensor2DTemplate&)

Tensor2DTemplate column(size_t j) const

get column #j as a 1-column Tensor2D

inline size_t numel() const

inline T *data()

inline const T *data() const

Public Members

size_t shape[2]

std::vector<T> v

struct Linear

#include <NeuralNet.h>

minimal translation of nn.Linear

Public Functions

Linear(size_t in_features, size_t out_features, bool bias = true)

Tensor2D operator()(const Tensor2D &x) const

Public Members

size_t in_features

size_t out_features

std::vector<float> weight

std::vector<float> bias

struct Embedding

#include <NeuralNet.h>

minimal translation of nn.Embedding

Public Functions

Embedding(size_t num_embeddings, size_t embedding_dim)

Tensor2D operator()(const Int32Tensor2D&) const

inline float *data()

inline const float *data() const

Public Members

size_t num_embeddings

size_t embedding_dim

std::vector<float> weight

struct FFN

#include <NeuralNet.h>

Feed forward layer that expands to a hidden dimension, applies a ReLU non linearity and maps back to the orignal dimension

Public Functions

inline FFN(int d, int h)

Tensor2D operator()(const Tensor2D &x) const

Public Members

Linear linear1

Linear linear2