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https://github.com/Relintai/pmlpp.git
synced 2025-01-08 17:29:36 +01:00
Added a copy of MLPPMLPOld as MLPPMLP.
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parent
abe878ee72
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259
mlpp/mlp/mlp.cpp
259
mlpp/mlp/mlp.cpp
@ -16,6 +16,265 @@
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#include <random>
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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) :
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inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
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MLPPActivation avn;
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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);
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bias1 = MLPPUtilities::biasInitialization(n_hidden);
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bias2 = MLPPUtilities::biasInitialization();
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}
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std::vector<real_t> MLPPMLP::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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}
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real_t MLPPMLP::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPMLP::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<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<real_t> D2_1 = alg.mat_vec_mult(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 / n, D2_1));
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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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bias2 -= learning_rate * alg.sum_elements(error) / n;
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// Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1;
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D1_1.resize(n);
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D1_1 = alg.outerProduct(error, 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 / n, 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 / n, 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 MLPPMLP::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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real_t y_hat = Evaluate(inputSet[outputIndex]);
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auto [z2, a2] = propagate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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real_t error = y_hat - outputSet[outputIndex];
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// Weight updation for layer 2
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std::vector<real_t> D2_1 = alg.scalarMultiply(error, a2);
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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 -= learning_rate * error;
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// Weight updation for layer 1
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std::vector<real_t> D1_1 = alg.scalarMultiply(error, weights2);
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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.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 MLPPMLP::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 [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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auto [z2, a2] = propagate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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// Calculating the errors
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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<real_t> D2_1 = alg.mat_vec_mult(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 / outputMiniBatches[i].size(), D2_1));
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weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
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// Calculating the bias gradients for layer 2
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real_t b_gradient = alg.sum_elements(error);
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// Bias Updation for layer 2
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bias2 -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
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//Calculating the weight/bias for layer 1
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std::vector<std::vector<real_t>> D1_1 = alg.outerProduct(error, 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 / outputMiniBatches[i].size(), 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 / outputMiniBatches[i].size(), 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 MLPPMLP::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 MLPPMLP::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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real_t MLPPMLP::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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MLPPReg regularization;
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class MLPPCost cost;
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return cost.LogLoss(y_hat, y) + regularization.regTerm(weights2, lambda, alpha, reg) + regularization.regTerm(weights1, lambda, alpha, reg);
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}
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std::vector<real_t> MLPPMLP::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.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
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}
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> MLPPMLP::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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real_t MLPPMLP::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.sigmoid(alg.dot(weights2, a2) + bias2);
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}
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std::tuple<std::vector<real_t>, std::vector<real_t>> MLPPMLP::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 MLPPMLP::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.sigmoid(alg.scalarAdd(bias2, alg.mat_vec_mult(a2, weights2)));
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}
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// ======= OLD =======
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MLPPMLPOld::MLPPMLPOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg, real_t lambda, real_t alpha) :
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inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
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MLPPActivation avn;
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@ -14,6 +14,49 @@
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#include <string>
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#include <vector>
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class MLPPMLP {
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public:
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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);
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std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
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real_t modelTest(std::vector<real_t> x);
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
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void SGD(real_t learning_rate, int max_epoch, bool UI = false);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
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real_t score();
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void save(std::string fileName);
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private:
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagate(std::vector<std::vector<real_t>> X);
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real_t Evaluate(std::vector<real_t> x);
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std::tuple<std::vector<real_t>, std::vector<real_t>> propagate(std::vector<real_t> x);
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void forwardPass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<real_t> outputSet;
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std::vector<real_t> y_hat;
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std::vector<std::vector<real_t>> weights1;
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std::vector<real_t> weights2;
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std::vector<real_t> bias1;
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real_t bias2;
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std::vector<std::vector<real_t>> z2;
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std::vector<std::vector<real_t>> a2;
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int n;
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int k;
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int n_hidden;
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// Regularization Params
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std::string reg;
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real_t lambda; /* Regularization Parameter */
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real_t alpha; /* This is the controlling param for Elastic Net*/
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};
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class MLPPMLPOld {
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public:
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MLPPMLPOld(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);
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