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Small improvements.

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This commit is contained in:
Relintai 2023-02-05 13:14:09 +01:00
parent b8e3f41fda
commit dc4da4681b
1 changed files with 13 additions and 11 deletions
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24
mlpp/mlp/mlp.cpp
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@ -6,6 +6,8 @@
#include "mlp.h"
#include "core/log/logger.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../lin_alg/lin_alg.h"
@ -123,9 +125,9 @@ void MLPPMLP::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
// UI PORTION
if (UI) {
MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, output_set));
std::cout << "Layer 1:" << std::endl;
PLOG_MSG("Layer 1:");
MLPPUtilities::print_ui_mb(weights1, bias1);
std::cout << "Layer 2:" << std::endl;
PLOG_MSG("Layer 2:");
MLPPUtilities::print_ui_vb(weights2, bias2);
}
@ -204,9 +206,9 @@ void MLPPMLP::sgd(real_t learning_rate, int max_epoch, bool UI) {
if (UI) {
MLPPUtilities::cost_info(epoch, cost_prev, cost_prev);
std::cout << "Layer 1:" << std::endl;
PLOG_MSG("Layer 1:");
MLPPUtilities::print_ui_mb(weights1, bias1);
std::cout << "Layer 2:" << std::endl;
PLOG_MSG("Layer 2:");
MLPPUtilities::print_ui_vb(weights2, bias2);
}
@ -281,9 +283,9 @@ void MLPPMLP::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
if (UI) {
MLPPUtilities::CostInfo(epoch, cost_prev, cost(ly_hat, current_output));
std::cout << "Layer 1:" << std::endl;
PLOG_MSG("Layer 1:");
MLPPUtilities::print_ui_mb(weights1, bias1);
std::cout << "Layer 2:" << std::endl;
PLOG_MSG("Layer 2:");
MLPPUtilities::print_ui_vb(weights2, bias2);
}
}
@ -562,7 +564,7 @@ void MLPPMLPOld::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
D1_1 = alg.outerProduct(error, weights2);
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputSet), D1_2);
@ -620,7 +622,7 @@ void MLPPMLPOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
// Weight updation for layer 1
std::vector<real_t> D1_1 = alg.scalarMultiply(error, weights2);
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));
@ -689,7 +691,7 @@ void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
std::vector<std::vector<real_t>> D1_1 = alg.outerProduct(error, weights2);
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
@ -724,8 +726,8 @@ real_t MLPPMLPOld::score() {
void MLPPMLPOld::save(std::string fileName) {
MLPPUtilities util;
util.saveParameters(fileName, weights1, bias1, 0, 1);
util.saveParameters(fileName, weights2, bias2, 1, 2);
util.saveParameters(fileName, weights1, bias1, false, 1);
util.saveParameters(fileName, weights2, bias2, true, 2);
}
real_t MLPPMLPOld::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {