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309 lines
10 KiB
C++
309 lines
10 KiB
C++
//
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// SoftmaxNet.cpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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#include "softmax_net.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../data/data.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include <iostream>
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#include <random>
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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) {
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inputSet = pinputSet;
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outputSet = poutputSet;
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n = pinputSet.size();
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k = pinputSet[0].size();
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n_hidden = pn_hidden;
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n_class = poutputSet[0].size();
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reg = preg;
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lambda = plambda;
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alpha = palpha;
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y_hat.resize(n);
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weights1 = MLPPUtilities::weightInitialization(k, n_hidden);
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weights2 = MLPPUtilities::weightInitialization(n_hidden, n_class);
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bias1 = MLPPUtilities::biasInitialization(n_hidden);
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bias2 = MLPPUtilities::biasInitialization(n_class);
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}
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std::vector<real_t> MLPPSoftmaxNet::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxNet::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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}
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void MLPPSoftmaxNet::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, outputSet);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
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// weights and bias updation for layer 2
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, D2_1));
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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputSet), D1_2);
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// weight an bias updation for layer 1
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weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
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weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
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bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate, D1_2));
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forwardPass();
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// UI PORTION
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(weights2, bias2);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPSoftmaxNet::SGD(real_t learning_rate, int max_epoch, bool UI) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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while (true) {
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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int outputIndex = distribution(generator);
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std::vector<real_t> y_hat = Evaluate(inputSet[outputIndex]);
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auto prop_res = propagate(inputSet[outputIndex]);
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auto z2 = std::get<0>(prop_res);
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auto a2 = std::get<1>(prop_res);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet[outputIndex]);
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// Weight updation for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.outerProduct(error, a2);
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, alg.transpose(D2_1)));
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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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// Bias updation for layer 2
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bias2 = alg.subtraction(bias2, alg.scalarMultiply(learning_rate, error));
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// Weight updation for layer 1
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std::vector<real_t> D1_1 = alg.mat_vec_mult(weights2, error);
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std::vector<real_t> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, true));
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std::vector<std::vector<real_t>> D1_3 = alg.outerProduct(inputSet[outputIndex], D1_2);
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weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
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weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
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// Bias updation for layer 1
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bias1 = alg.subtraction(bias1, alg.scalarMultiply(learning_rate, D1_2));
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y_hat = Evaluate(inputSet[outputIndex]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(weights2, bias2);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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}
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void MLPPSoftmaxNet::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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// Creating the mini-batches
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<std::vector<real_t>> currentInputSet;
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std::vector<std::vector<real_t>> currentOutputSet;
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for (int j = 0; j < n / n_mini_batch; j++) {
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currentInputSet.push_back(inputSet[n / n_mini_batch * i + j]);
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currentOutputSet.push_back(outputSet[n / n_mini_batch * i + j]);
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}
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inputMiniBatches.push_back(currentInputSet);
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outputMiniBatches.push_back(currentOutputSet);
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}
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if (real_t(n) / real_t(n_mini_batch) - int(n / n_mini_batch) != 0) {
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for (int i = 0; i < n - n / n_mini_batch * n_mini_batch; i++) {
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inputMiniBatches[n_mini_batch - 1].push_back(inputSet[n / n_mini_batch * n_mini_batch + i]);
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outputMiniBatches[n_mini_batch - 1].push_back(outputSet[n / n_mini_batch * n_mini_batch + i]);
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}
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}
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<std::vector<real_t>> y_hat = Evaluate(inputMiniBatches[i]);
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auto propagate_res = propagate(inputMiniBatches[i]);
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auto z2 = std::get<0>(propagate_res);
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auto a2 = std::get<1>(propagate_res);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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// Calculating the errors
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std::vector<std::vector<real_t>> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight/bias gradients for layer 2
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std::vector<std::vector<real_t>> D2_1 = alg.matmult(alg.transpose(a2), error);
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// weights and bias updation for layser 2
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weights2 = alg.subtraction(weights2, alg.scalarMultiply(learning_rate, D2_1));
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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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// Bias Updation for layer 2
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bias2 = alg.subtractMatrixRows(bias2, alg.scalarMultiply(learning_rate, error));
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.matmult(error, alg.transpose(weights2));
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std::vector<std::vector<real_t>> D1_2 = alg.hadamard_product(D1_1, avn.sigmoid(z2, 1));
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std::vector<std::vector<real_t>> D1_3 = alg.matmult(alg.transpose(inputMiniBatches[i]), D1_2);
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// weight an bias updation for layer 1
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weights1 = alg.subtraction(weights1, alg.scalarMultiply(learning_rate, D1_3));
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weights1 = regularization.regWeights(weights1, lambda, alpha, reg);
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bias1 = alg.subtractMatrixRows(bias1, alg.scalarMultiply(learning_rate, D1_2));
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y_hat = Evaluate(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
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std::cout << "Layer 1:" << std::endl;
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MLPPUtilities::UI(weights1, bias1);
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std::cout << "Layer 2:" << std::endl;
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MLPPUtilities::UI(weights2, bias2);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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}
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real_t MLPPSoftmaxNet::score() {
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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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}
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void MLPPSoftmaxNet::save(std::string fileName) {
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MLPPUtilities util;
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util.saveParameters(fileName, weights1, bias1, 0, 1);
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util.saveParameters(fileName, weights2, bias2, 1, 2);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxNet::getEmbeddings() {
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return weights1;
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}
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real_t MLPPSoftmaxNet::Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y) {
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MLPPReg regularization;
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MLPPData data;
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class MLPPCost cost;
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return cost.CrossEntropy(y_hat, y) + regularization.regTerm(weights1, lambda, alpha, reg) + regularization.regTerm(weights2, lambda, alpha, reg);
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}
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std::vector<std::vector<real_t>> MLPPSoftmaxNet::Evaluate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
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std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
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return avn.adjSoftmax(alg.mat_vec_add(alg.matmult(a2, weights2), bias2));
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}
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPSoftmaxNet::propagate(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<std::vector<real_t>> z2 = alg.mat_vec_add(alg.matmult(X, weights1), bias1);
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std::vector<std::vector<real_t>> a2 = avn.sigmoid(z2);
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return { z2, a2 };
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}
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std::vector<real_t> MLPPSoftmaxNet::Evaluate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
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std::vector<real_t> a2 = avn.sigmoid(z2);
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return avn.adjSoftmax(alg.addition(alg.mat_vec_mult(alg.transpose(weights2), a2), bias2));
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}
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std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPSoftmaxNet::propagate(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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std::vector<real_t> z2 = alg.addition(alg.mat_vec_mult(alg.transpose(weights1), x), bias1);
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std::vector<real_t> a2 = avn.sigmoid(z2);
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return { z2, a2 };
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}
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void MLPPSoftmaxNet::forwardPass() {
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MLPPLinAlg alg;
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MLPPActivation avn;
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z2 = alg.mat_vec_add(alg.matmult(inputSet, weights1), bias1);
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a2 = avn.sigmoid(z2);
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y_hat = avn.adjSoftmax(alg.mat_vec_add(alg.matmult(a2, weights2), bias2));
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}
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