Relintai/pmlpp
1
0
Fork 0
mirror of https://github.com/Relintai/pmlpp.git synced 2025-02-01 17:07:02 +01:00
Code Issues Packages Projects Releases Wiki Activity

Fix warnings in MLPPSoftmaxNet.

Browse Source
This commit is contained in:
Relintai 2023-02-10 21:26:46 +01:00
parent 628e5124e9
commit e51a976a10
2 changed files with 27 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

37
mlpp/softmax_net/softmax_net.cpp
View File

@ -15,9 +15,17 @@
#include <iostream>
#include <random>
MLPPSoftmaxNet::MLPPSoftmaxNet(std::vector<std::vector<real_t>> pinputSet, std::vector<std::vector<real_t>> poutputSet, int pn_hidden, std::string preg, real_t plambda, real_t palpha) {
inputSet = pinputSet;
outputSet = poutputSet;
n = pinputSet.size();
k = pinputSet[0].size();
n_hidden = pn_hidden;
n_class = poutputSet[0].size();
reg = preg;
lambda = plambda;
alpha = palpha;
MLPPSoftmaxNet::MLPPSoftmaxNet(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, int n_hidden, std::string reg, real_t lambda, real_t alpha) :
inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), n_hidden(n_hidden), n_class(outputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
y_hat.resize(n);
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
@ -104,7 +112,11 @@ void MLPPSoftmaxNet::SGD(real_t learning_rate, int max_epoch, bool UI) {
int outputIndex = distribution(generator);
std::vector<real_t> y_hat = Evaluate(inputSet[outputIndex]);
auto [z2, a2] = propagate(inputSet[outputIndex]);
auto prop_res = propagate(inputSet[outputIndex]);
auto z2 = std::get<0>(prop_res);
auto a2 = std::get<1>(prop_res);
cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
std::vector<real_t> error = alg.subtraction(y_hat, outputSet[outputIndex]);
@ -118,7 +130,7 @@ void MLPPSoftmaxNet::SGD(real_t learning_rate, int max_epoch, bool UI) {
// Weight updation for layer 1
std::vector<real_t> D1_1 = alg.mat_vec_mult(weights2, error);
std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
@ -153,7 +165,10 @@ void MLPPSoftmaxNet::MBGD(real_t learning_rate, int max_epoch, int mini_batch_si
// 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);
// Creating the mini-batches
for (int i = 0; i < n_mini_batch; i++) {
@ -177,7 +192,11 @@ void MLPPSoftmaxNet::MBGD(real_t learning_rate, int max_epoch, int mini_batch_si
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]);
auto [z2, a2] = propagate(inputMiniBatches[i]);
auto propagate_res = propagate(inputMiniBatches[i]);
auto z2 = std::get<0>(propagate_res);
auto a2 = std::get<1>(propagate_res);
cost_prev = Cost(y_hat, outputMiniBatches[i]);
// Calculating the errors
@ -227,16 +246,14 @@ void MLPPSoftmaxNet::MBGD(real_t learning_rate, int max_epoch, int mini_batch_si
}
real_t MLPPSoftmaxNet::score() {
MLPPUtilities util;
MLPPUtilities util;
return util.performance(y_hat, outputSet);
}
void MLPPSoftmaxNet::save(std::string fileName) {
MLPPUtilities util;
MLPPUtilities util;
util.saveParameters(fileName, weights1, bias1, 0, 1);
util.saveParameters(fileName, weights2, bias2, 1, 2);
MLPPLinAlg alg;
}
std::vector<std::vector<real_t>> MLPPSoftmaxNet::getEmbeddings() {

4
mlpp/softmax_net/softmax_net.h
View File

@ -1,4 +1,3 @@
#ifndef MLPP_SOFTMAX_NET_H
#define MLPP_SOFTMAX_NET_H
@ -13,8 +12,6 @@
#include <string>
#include <vector>
class MLPPSoftmaxNet {
public:
MLPPSoftmaxNet(std::vector<std::vector<real_t>> inputSet, std::vector<std::vector<real_t>> outputSet, int n_hidden, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
@ -60,5 +57,4 @@ private:
real_t alpha; /* This is the controlling param for Elastic Net*/
};
#endif /* SoftmaxNet_hpp */