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Fixed warnings in MLPPAutoEncoder.

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This commit is contained in:
Relintai 2023-02-10 20:05:47 +01:00
parent 17d3f486ae
commit 3a56ed59e3
2 changed files with 31 additions and 16 deletions
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41
mlpp/auto_encoder/auto_encoder.cpp
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@ -13,17 +13,6 @@
#include <iostream> #include <iostream>
#include <random> #include <random>
MLPPAutoEncoder::MLPPAutoEncoder(std::vector<std::vector<real_t>> inputSet, int n_hidden) :
inputSet(inputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()) {
MLPPActivation avn;
y_hat.resize(inputSet.size());
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
weights2 = MLPPUtilities::weightInitialization(n_hidden, k);
bias1 = MLPPUtilities::biasInitialization(n_hidden);
bias2 = MLPPUtilities::biasInitialization(k);
}
std::vector<std::vector<real_t>> MLPPAutoEncoder::modelSetTest(std::vector<std::vector<real_t>> X) { std::vector<std::vector<real_t>> MLPPAutoEncoder::modelSetTest(std::vector<std::vector<real_t>> X) {
return Evaluate(X); return Evaluate(X);
} }
@ -98,7 +87,10 @@ void MLPPAutoEncoder::SGD(real_t learning_rate, int max_epoch, bool UI) {
int outputIndex = distribution(generator); int outputIndex = distribution(generator);
std::vector<real_t> y_hat = Evaluate(inputSet[outputIndex]); 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 }, { inputSet[outputIndex] }); cost_prev = Cost({ y_hat }, { inputSet[outputIndex] });
std::vector<real_t> error = alg.subtraction(y_hat, inputSet[outputIndex]); std::vector<real_t> error = alg.subtraction(y_hat, inputSet[outputIndex]);
@ -149,7 +141,11 @@ void MLPPAutoEncoder::MBGD(real_t learning_rate, int max_epoch, int mini_batch_s
while (true) { while (true) {
for (int i = 0; i < n_mini_batch; i++) { for (int i = 0; i < n_mini_batch; i++) {
std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]); std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]);
auto [z2, a2] = propagate(inputMiniBatches[i]);
auto prop_res = propagate(inputMiniBatches[i]);
auto z2 = std::get<0>(prop_res);
auto a2 = std::get<1>(prop_res);
cost_prev = Cost(y_hat, inputMiniBatches[i]); cost_prev = Cost(y_hat, inputMiniBatches[i]);
// Calculating the errors // Calculating the errors
@ -197,16 +193,31 @@ void MLPPAutoEncoder::MBGD(real_t learning_rate, int max_epoch, int mini_batch_s
} }
real_t MLPPAutoEncoder::score() { real_t MLPPAutoEncoder::score() {
MLPPUtilities util; MLPPUtilities util;
return util.performance(y_hat, inputSet); return util.performance(y_hat, inputSet);
} }
void MLPPAutoEncoder::save(std::string fileName) { void MLPPAutoEncoder::save(std::string fileName) {
MLPPUtilities util; MLPPUtilities util;
util.saveParameters(fileName, weights1, bias1, 0, 1); util.saveParameters(fileName, weights1, bias1, 0, 1);
util.saveParameters(fileName, weights2, bias2, 1, 2); util.saveParameters(fileName, weights2, bias2, 1, 2);
} }
MLPPAutoEncoder::MLPPAutoEncoder(std::vector<std::vector<real_t>> pinputSet, int pn_hidden) {
inputSet = pinputSet;
n_hidden = pn_hidden;
n = inputSet.size();
k = inputSet[0].size();
MLPPActivation avn;
y_hat.resize(inputSet.size());
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
weights2 = MLPPUtilities::weightInitialization(n_hidden, k);
bias1 = MLPPUtilities::biasInitialization(n_hidden);
bias2 = MLPPUtilities::biasInitialization(k);
}
real_t MLPPAutoEncoder::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) { real_t MLPPAutoEncoder::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
class MLPPCost cost; class MLPPCost cost;
return cost.MSE(y_hat, inputSet); return cost.MSE(y_hat, inputSet);

6
mlpp/auto_encoder/auto_encoder.h
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@ -16,15 +16,19 @@
class MLPPAutoEncoder { class MLPPAutoEncoder {
public: public:
MLPPAutoEncoder(std::vector<std::vector<real_t>> inputSet, int n_hidden);
std::vector<std::vector<real_t>> modelSetTest(std::vector<std::vector<real_t>> X); std::vector<std::vector<real_t>> modelSetTest(std::vector<std::vector<real_t>> X);
std::vector<real_t> modelTest(std::vector<real_t> x); std::vector<real_t> modelTest(std::vector<real_t> x);
void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false); void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
void SGD(real_t learning_rate, int max_epoch, bool UI = false); void SGD(real_t learning_rate, int max_epoch, bool UI = false);
void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false); void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
real_t score(); real_t score();
void save(std::string fileName); void save(std::string fileName);
MLPPAutoEncoder(std::vector<std::vector<real_t>> inputSet, int n_hidden);
private: private:
real_t Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y); real_t Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);