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Fixed warnings in MLPPTanhReg.

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Relintai 2023-02-10 20:53:26 +01:00
parent 4e30b31833
commit e191ab9a16
2 changed files with 7 additions and 8 deletions
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12
mlpp/tanh_reg/tanh_reg.cpp
View File

@ -5,6 +5,7 @@
//
#include "tanh_reg.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
@ -14,7 +15,6 @@
#include <iostream>
#include <random>
MLPPTanhReg::MLPPTanhReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t alpha) :
inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
y_hat.resize(n);
@ -107,12 +107,15 @@ void MLPPTanhReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
MLPPActivation avn;
MLPPLinAlg alg;
MLPPReg regularization;
real_t cost_prev = 0;
int epoch = 1;
// Creating the mini-batches
int n_mini_batch = n / mini_batch_size;
auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto inputMiniBatches = std::get<0>(batches);
auto outputMiniBatches = std::get<1>(batches);
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
@ -147,12 +150,12 @@ void MLPPTanhReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
}
real_t MLPPTanhReg::score() {
MLPPUtilities util;
MLPPUtilities util;
return util.performance(y_hat, outputSet);
}
void MLPPTanhReg::save(std::string fileName) {
MLPPUtilities util;
MLPPUtilities util;
util.saveParameters(fileName, weights, bias);
}
@ -186,7 +189,6 @@ real_t MLPPTanhReg::propagate(std::vector<real_t> x) {
// Tanh ( wTx + b )
void MLPPTanhReg::forwardPass() {
MLPPLinAlg alg;
MLPPActivation avn;
z = propagate(inputSet);

3
mlpp/tanh_reg/tanh_reg.h
View File

@ -13,8 +13,6 @@
#include <string>
#include <vector>
class MLPPTanhReg {
public:
MLPPTanhReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
@ -54,5 +52,4 @@ private:
real_t alpha; /* This is the controlling param for Elastic Net*/
};
#endif /* TanhReg_hpp */