#ifndef PROOF_CUH
#define PROOF_CUH
#include "fr-tensor.cuh"
#include "g1-tensor.cuh"
#include "commitment.cuh"
#include "bls12-381.cuh"
#include "polynomial.cuh"
#include <vector>
#include <random>
struct Claim {
Fr_t claim;
std
::vector<std
::vector<Fr_t>> u;
std
::vector<uint> dims;
};
struct Weight;
void verifyWeightClaim
(const Weight& w, const Claim& c
);
// struct Weight {
// Commitment generator;
// FrTensor weight;
// G1TensorJacobian com;
// };
KERNEL void Fr_
ip_sc_step
(GLOBAL Fr_t *a, GLOBAL Fr_t *b, GLOBAL Fr_t *out0, GLOBAL Fr_t *out1, GLOBAL Fr_t *out2, uint in_size, uint out_size
);
void Fr_
ip_sc
(const FrTensor& a, const FrTensor& b, vector<Fr_t>
::const_iterator begin, vector<Fr_t>
::const_iterator end, vector<Fr_t>& proof
);
vector<Fr_t>
inner_
product_sumcheck
(const FrTensor& a, const FrTensor& b, vector<Fr_t> u
);
void Fr_hp_sc
(const FrTensor& a, const FrTensor& b, vector<Fr_t>
::const_iterator u_begin, vector<Fr_t>
::const_iterator u_end, vector<Fr_t>
::const_iterator v_begin, vector<Fr_t>
::const_iterator v_end, vector<Fr_t>& proof
);
vector<Fr_t> hadamard_
product_sumcheck
(const FrTensor& a, const FrTensor& b, vector<Fr_t> u, vector<Fr_t> v
);
KERNEL void Fr_bin_sc_step
(GLOBAL Fr_t *a, GLOBAL Fr_t *out0, GLOBAL Fr_t *out1, GLOBAL Fr_t *out2, uint in_size, uint out_size
);
void Fr_bin_sc
(const FrTensor& a, vector<Fr_t>
::const_iterator u_begin, vector<Fr_t>
::const_iterator u_end, vector<Fr_t>
::const_iterator v_begin, vector<Fr_t>
::const_iterator v_end, vector<Fr_t>& proof
);
vector<Fr_t> binary_sumcheck
(const FrTensor& a, vector<Fr_t> u, vector<Fr_t> v
);
bool operator==
(const Fr_t& a, const Fr_t& b
);
bool operator!=
(const Fr_t& a, const Fr_t& b
);
Fr_t multi_hadamard_sumchecks
(const Fr_t& claim, const vector<FrTensor>& Xs, const vector<Fr_t>& u, const vector<Fr_t>& v, vector<Polynomial>& proof
);
#endif解释这个代码
本文介绍了Caffe框架中Inner_Product层的概念，该层等同于全连接(fully_connected)层，是深度学习模型中常见的一种层类型。
扫一扫
506
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
