mirror of
https://github.com/Relintai/pmlpp.git
synced 2025-03-12 22:38:51 +01:00
Initial cleanups to MLP.
This commit is contained in:
parent
a875cc9e70
commit
5f63aebc99
@ -15,36 +15,25 @@
|
||||
#include <iostream>
|
||||
#include <random>
|
||||
|
||||
|
||||
MLPPMLP::MLPPMLP(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg, real_t lambda, real_t alpha) :
|
||||
inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
|
||||
MLPPActivation avn;
|
||||
y_hat.resize(n);
|
||||
|
||||
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
|
||||
weights2 = MLPPUtilities::weightInitialization(n_hidden);
|
||||
bias1 = MLPPUtilities::biasInitialization(n_hidden);
|
||||
bias2 = MLPPUtilities::biasInitialization();
|
||||
std::vector<real_t> MLPPMLP::model_set_test(std::vector<std::vector<real_t>> X) {
|
||||
return evaluate(X);
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPMLP::modelSetTest(std::vector<std::vector<real_t>> X) {
|
||||
return Evaluate(X);
|
||||
real_t MLPPMLP::model_test(std::vector<real_t> x) {
|
||||
return evaluate(x);
|
||||
}
|
||||
|
||||
real_t MLPPMLP::modelTest(std::vector<real_t> x) {
|
||||
return Evaluate(x);
|
||||
}
|
||||
|
||||
void MLPPMLP::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
|
||||
void MLPPMLP::gradient_descent(real_t learning_rate, int max_epoch, bool UI) {
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
|
||||
while (true) {
|
||||
cost_prev = Cost(y_hat, outputSet);
|
||||
cost_prev = cost(y_hat, outputSet);
|
||||
|
||||
// Calculating the errors
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
|
||||
@ -76,11 +65,11 @@ void MLPPMLP::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
|
||||
|
||||
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / n, D1_2));
|
||||
|
||||
forwardPass();
|
||||
forward_pass();
|
||||
|
||||
// UI PORTION
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputSet));
|
||||
std::cout << "Layer 1:" << std::endl;
|
||||
MLPPUtilities::UI(weights1, bias1);
|
||||
std::cout << "Layer 2:" << std::endl;
|
||||
@ -94,7 +83,7 @@ void MLPPMLP::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPMLP::SGD(real_t learning_rate, int max_epoch, bool UI) {
|
||||
void MLPPMLP::sgd(real_t learning_rate, int max_epoch, bool UI) {
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
@ -107,9 +96,9 @@ void MLPPMLP::SGD(real_t learning_rate, int max_epoch, bool UI) {
|
||||
std::uniform_int_distribution<int> distribution(0, int(n - 1));
|
||||
int outputIndex = distribution(generator);
|
||||
|
||||
real_t y_hat = Evaluate(inputSet[outputIndex]);
|
||||
real_t y_hat = evaluate(inputSet[outputIndex]);
|
||||
auto [z2, a2] = propagate(inputSet[outputIndex]);
|
||||
cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
|
||||
cost_prev = cost({ y_hat }, { outputSet[outputIndex] });
|
||||
real_t error = y_hat - outputSet[outputIndex];
|
||||
|
||||
// Weight updation for layer 2
|
||||
@ -131,9 +120,9 @@ void MLPPMLP::SGD(real_t learning_rate, int max_epoch, bool UI) {
|
||||
|
||||
bias1 = alg.subtraction(bias1, alg.scalarMultiply(learning_rate, D1_2));
|
||||
|
||||
y_hat = Evaluate(inputSet[outputIndex]);
|
||||
y_hat = evaluate(inputSet[outputIndex]);
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { outputSet[outputIndex] }));
|
||||
std::cout << "Layer 1:" << std::endl;
|
||||
MLPPUtilities::UI(weights1, bias1);
|
||||
std::cout << "Layer 2:" << std::endl;
|
||||
@ -145,10 +134,11 @@ void MLPPMLP::SGD(real_t learning_rate, int max_epoch, bool UI) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPMLP::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
|
||||
void MLPPMLP::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
|
||||
MLPPActivation avn;
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
@ -161,9 +151,9 @@ void MLPPMLP::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, boo
|
||||
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = evaluate(inputMiniBatches[i]);
|
||||
auto [z2, a2] = propagate(inputMiniBatches[i]);
|
||||
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
|
||||
// Calculating the errors
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
|
||||
@ -196,42 +186,45 @@ void MLPPMLP::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, boo
|
||||
|
||||
bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), D1_2));
|
||||
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
y_hat = evaluate(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
|
||||
std::cout << "Layer 1:" << std::endl;
|
||||
MLPPUtilities::UI(weights1, bias1);
|
||||
std::cout << "Layer 2:" << std::endl;
|
||||
MLPPUtilities::UI(weights2, bias2);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
real_t MLPPMLP::score() {
|
||||
MLPPUtilities util;
|
||||
MLPPUtilities util;
|
||||
return util.performance(y_hat, outputSet);
|
||||
}
|
||||
|
||||
void MLPPMLP::save(std::string fileName) {
|
||||
MLPPUtilities util;
|
||||
MLPPUtilities util;
|
||||
util.saveParameters(fileName, weights1, bias1, 0, 1);
|
||||
util.saveParameters(fileName, weights2, bias2, 1, 2);
|
||||
}
|
||||
|
||||
real_t MLPPMLP::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||
real_t MLPPMLP::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||
MLPPReg regularization;
|
||||
class MLPPCost cost;
|
||||
return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg);
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPMLP::Evaluate(std::vector<std::vector<real_t>> X) {
|
||||
std::vector<real_t> MLPPMLP::evaluate(std::vector<std::vector<real_t>> X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
|
||||
@ -247,7 +240,7 @@ std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> M
|
||||
return { z2, a2 };
|
||||
}
|
||||
|
||||
real_t MLPPMLP::Evaluate(std::vector<real_t> x) {
|
||||
real_t MLPPMLP::evaluate(std::vector<real_t> x) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
|
||||
@ -263,7 +256,7 @@ std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPMLP::propagate(std::vec
|
||||
return { z2, a2 };
|
||||
}
|
||||
|
||||
void MLPPMLP::forwardPass() {
|
||||
void MLPPMLP::forward_pass() {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);
|
||||
@ -271,7 +264,21 @@ void MLPPMLP::forwardPass() {
|
||||
y_hat = avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
|
||||
}
|
||||
|
||||
MLPPMLP::MLPPMLP(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg, real_t lambda, real_t alpha) :
|
||||
inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
|
||||
MLPPActivation avn;
|
||||
y_hat.resize(n);
|
||||
|
||||
weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
|
||||
weights2 = MLPPUtilities::weightInitialization(n_hidden);
|
||||
bias1 = MLPPUtilities::biasInitialization(n_hidden);
|
||||
bias2 = MLPPUtilities::biasInitialization();
|
||||
}
|
||||
|
||||
MLPPMLP::MLPPMLP() {
|
||||
}
|
||||
MLPPMLP::~MLPPMLP() {
|
||||
}
|
||||
|
||||
// ======= OLD =======
|
||||
|
||||
@ -474,12 +481,12 @@ void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
}
|
||||
|
||||
real_t MLPPMLPOld::score() {
|
||||
MLPPUtilities util;
|
||||
MLPPUtilities util;
|
||||
return util.performance(y_hat, outputSet);
|
||||
}
|
||||
|
||||
void MLPPMLPOld::save(std::string fileName) {
|
||||
MLPPUtilities util;
|
||||
MLPPUtilities util;
|
||||
util.saveParameters(fileName, weights1, bias1, 0, 1);
|
||||
util.saveParameters(fileName, weights2, bias2, 1, 2);
|
||||
}
|
||||
@ -529,4 +536,3 @@ void MLPPMLPOld::forwardPass() {
|
||||
a2 = avn.sigmoid(z2);
|
||||
y_hat = avn.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
|
||||
}
|
||||
|
||||
|
||||
@ -8,31 +8,48 @@
|
||||
// Created by Marc Melikyan on 11/4/20.
|
||||
//
|
||||
|
||||
#include "core/containers/vector.h"
|
||||
#include "core/math/math_defs.h"
|
||||
#include "core/string/ustring.h"
|
||||
#include "core/variant/variant.h"
|
||||
|
||||
#include "core/object/reference.h"
|
||||
|
||||
#include "../lin_alg/mlpp_matrix.h"
|
||||
#include "../lin_alg/mlpp_vector.h"
|
||||
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
class MLPPMLP {
|
||||
class MLPPMLP : public Reference {
|
||||
GDCLASS(MLPPMLP, Reference);
|
||||
|
||||
public:
|
||||
MLPPMLP(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
|
||||
real_t modelTest(std::vector<real_t> x);
|
||||
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 MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
|
||||
std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
|
||||
real_t model_test(std::vector<real_t> x);
|
||||
|
||||
void gradient_descent(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);
|
||||
|
||||
real_t score();
|
||||
void save(std::string fileName);
|
||||
|
||||
private:
|
||||
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
MLPPMLP(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
|
||||
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
|
||||
MLPPMLP();
|
||||
~MLPPMLP();
|
||||
|
||||
private:
|
||||
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
|
||||
std::vector<real_t> evaluate(std::vector<std::vector<real_t>> X);
|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagate(std::vector<std::vector<real_t>> X);
|
||||
real_t Evaluate(std::vector<real_t> x);
|
||||
real_t evaluate(std::vector<real_t> x);
|
||||
std::tuple<std::vector<real_t>, std::vector<real_t>> propagate(std::vector<real_t> x);
|
||||
void forwardPass();
|
||||
|
||||
void forward_pass();
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<real_t> outputSet;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user