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Initial cleanup pass on MLPPExpReg.
This commit is contained in:
parent
689fbd397f
commit
9a529c572d
@ -14,78 +14,68 @@
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#include <iostream>
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#include <random>
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MLPPExpReg::MLPPExpReg(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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n = p_inputSet.size();
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k = p_inputSet[0].size();
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reg = p_reg;
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lambda = p_lambda;
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alpha = p_alpha;
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y_hat.resize(n);
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weights = MLPPUtilities::weightInitialization(k);
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initial = MLPPUtilities::weightInitialization(k);
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bias = MLPPUtilities::biasInitialization();
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std::vector<real_t> MLPPExpReg::model_set_test(std::vector<std::vector<real_t>> X) {
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return evaluatem(X);
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}
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std::vector<real_t> MLPPExpReg::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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real_t MLPPExpReg::model_test(std::vector<real_t> x) {
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return evaluatev(x);
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}
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real_t MLPPExpReg::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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}
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void MLPPExpReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPExpReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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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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forward_pass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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for (int i = 0; i < k; i++) {
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for (int i = 0; i < _k; i++) {
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// Calculating the weight gradient
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real_t sum = 0;
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for (int j = 0; j < n; j++) {
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sum += error[j] * inputSet[j][i] * std::pow(weights[i], inputSet[j][i] - 1);
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for (int j = 0; j < _n; j++) {
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sum += error[j] * _input_set[j][i] * std::pow(_weights[i], _input_set[j][i] - 1);
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}
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real_t w_gradient = sum / n;
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real_t w_gradient = sum / _n;
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// Calculating the initial gradient
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real_t sum2 = 0;
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for (int j = 0; j < n; j++) {
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sum2 += error[j] * std::pow(weights[i], inputSet[j][i]);
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for (int j = 0; j < _n; j++) {
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sum2 += error[j] * std::pow(_weights[i], _input_set[j][i]);
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}
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real_t i_gradient = sum2 / n;
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real_t i_gradient = sum2 / _n;
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// Weight/initial updation
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weights[i] -= learning_rate * w_gradient;
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initial[i] -= learning_rate * i_gradient;
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_weights[i] -= learning_rate * w_gradient;
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_initial[i] -= learning_rate * i_gradient;
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}
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradient
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real_t sum = 0;
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for (int j = 0; j < n; j++) {
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sum += (y_hat[j] - outputSet[j]);
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for (int j = 0; j < _n; j++) {
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sum += (_y_hat[j] - _output_set[j]);
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}
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real_t b_gradient = sum / n;
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real_t b_gradient = sum / _n;
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// bias updation
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bias -= learning_rate * b_gradient;
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forwardPass();
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_bias -= learning_rate * b_gradient;
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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forward_pass();
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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@ -94,153 +84,184 @@ void MLPPExpReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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}
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}
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void MLPPExpReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPExpReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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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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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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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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int output_index = distribution(generator);
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real_t y_hat = Evaluate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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real_t y_hat = evaluatev(_input_set[output_index]);
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cost_prev = cost({ y_hat }, { _output_set[output_index] });
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for (int i = 0; i < k; i++) {
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for (int i = 0; i < _k; i++) {
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// Calculating the weight gradients
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real_t w_gradient = (y_hat - outputSet[outputIndex]) * inputSet[outputIndex][i] * std::pow(weights[i], inputSet[outputIndex][i] - 1);
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real_t i_gradient = (y_hat - outputSet[outputIndex]) * std::pow(weights[i], inputSet[outputIndex][i]);
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real_t w_gradient = (y_hat - _output_set[output_index]) * _input_set[output_index][i] * std::pow(_weights[i], _input_set[output_index][i] - 1);
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real_t i_gradient = (y_hat - _output_set[output_index]) * std::pow(_weights[i], _input_set[output_index][i]);
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// Weight/initial updation
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weights[i] -= learning_rate * w_gradient;
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initial[i] -= learning_rate * i_gradient;
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_weights[i] -= learning_rate * w_gradient;
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_initial[i] -= learning_rate * i_gradient;
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}
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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real_t b_gradient = (y_hat - outputSet[outputIndex]);
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real_t b_gradient = (y_hat - _output_set[output_index]);
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// Bias updation
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bias -= learning_rate * b_gradient;
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y_hat = Evaluate({ inputSet[outputIndex] });
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_bias -= learning_rate * b_gradient;
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y_hat = evaluatev(_input_set[output_index]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[output_index] }));
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MLPPUtilities::UI(_weights, _bias);
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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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forward_pass();
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}
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void MLPPExpReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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void MLPPExpReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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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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int n_mini_batch = _n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto input_mini_batches = std::get<0>(batches);
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auto output_mini_batches = std::get<1>(batches);
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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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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
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cost_prev = cost(y_hat, output_mini_batches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
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for (int j = 0; j < k; j++) {
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for (int j = 0; j < _k; j++) {
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// Calculating the weight gradient
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real_t sum = 0;
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for (uint32_t k = 0; k < outputMiniBatches[i].size(); k++) {
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sum += error[k] * inputMiniBatches[i][k][j] * std::pow(weights[j], inputMiniBatches[i][k][j] - 1);
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for (uint32_t k = 0; k < output_mini_batches[i].size(); k++) {
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sum += error[k] * input_mini_batches[i][k][j] * std::pow(_weights[j], input_mini_batches[i][k][j] - 1);
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}
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real_t w_gradient = sum / outputMiniBatches[i].size();
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real_t w_gradient = sum / output_mini_batches[i].size();
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// Calculating the initial gradient
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real_t sum2 = 0;
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for (uint32_t k = 0; k < outputMiniBatches[i].size(); k++) {
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sum2 += error[k] * std::pow(weights[j], inputMiniBatches[i][k][j]);
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for (uint32_t k = 0; k < output_mini_batches[i].size(); k++) {
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sum2 += error[k] * std::pow(_weights[j], input_mini_batches[i][k][j]);
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}
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real_t i_gradient = sum2 / outputMiniBatches[i].size();
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real_t i_gradient = sum2 / output_mini_batches[i].size();
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// Weight/initial updation
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weights[j] -= learning_rate * w_gradient;
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initial[j] -= learning_rate * i_gradient;
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_weights[j] -= learning_rate * w_gradient;
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_initial[j] -= learning_rate * i_gradient;
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}
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradient
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real_t sum = 0;
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for (uint32_t j = 0; j < outputMiniBatches[i].size(); j++) {
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sum += (y_hat[j] - outputMiniBatches[i][j]);
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for (uint32_t j = 0; j < output_mini_batches[i].size(); j++) {
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sum += (y_hat[j] - output_mini_batches[i][j]);
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}
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//real_t b_gradient = sum / outputMiniBatches[i].size();
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y_hat = Evaluate(inputMiniBatches[i]);
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//real_t b_gradient = sum / output_mini_batches[i].size();
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y_hat = evaluatem(input_mini_batches[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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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
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MLPPUtilities::UI(_weights, _bias);
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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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forward_pass();
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}
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real_t MLPPExpReg::score() {
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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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return util.performance(_y_hat, _output_set);
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}
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void MLPPExpReg::save(std::string fileName) {
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void MLPPExpReg::save(std::string file_name) {
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MLPPUtilities util;
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util.saveParameters(fileName, weights, initial, bias);
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util.saveParameters(file_name, _weights, _initial, _bias);
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}
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real_t MLPPExpReg::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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MLPPExpReg::MLPPExpReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n = p_input_set.size();
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_k = p_input_set[0].size();
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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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_y_hat.resize(_n);
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_weights = MLPPUtilities::weightInitialization(_k);
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_initial = MLPPUtilities::weightInitialization(_k);
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_bias = MLPPUtilities::biasInitialization();
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}
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real_t MLPPExpReg::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.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
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MLPPCost mlpp_cost;
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return mlpp_cost.MSE(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
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}
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std::vector<real_t> MLPPExpReg::Evaluate(std::vector<std::vector<real_t>> X) {
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real_t MLPPExpReg::evaluatev(std::vector<real_t> x) {
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real_t y_hat = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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y_hat += _initial[i] * std::pow(_weights[i], x[i]);
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}
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return y_hat + _bias;
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}
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std::vector<real_t> MLPPExpReg::evaluatem(std::vector<std::vector<real_t>> X) {
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std::vector<real_t> y_hat;
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y_hat.resize(X.size());
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for (uint32_t i = 0; i < X.size(); i++) {
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y_hat[i] = 0;
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for (uint32_t j = 0; j < X[i].size(); j++) {
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y_hat[i] += initial[j] * std::pow(weights[j], X[i][j]);
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y_hat[i] += _initial[j] * std::pow(_weights[j], X[i][j]);
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}
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y_hat[i] += bias;
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y_hat[i] += _bias;
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}
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return y_hat;
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}
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real_t MLPPExpReg::Evaluate(std::vector<real_t> x) {
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real_t y_hat = 0;
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for (uint32_t i = 0; i < x.size(); i++) {
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y_hat += initial[i] * std::pow(weights[i], x[i]);
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}
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return y_hat + bias;
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}
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// a * w^x + b
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void MLPPExpReg::forwardPass() {
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y_hat = Evaluate(inputSet);
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void MLPPExpReg::forward_pass() {
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_y_hat = evaluatem(_input_set);
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}
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@ -15,36 +15,41 @@
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class MLPPExpReg {
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public:
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MLPPExpReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, 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 = 1);
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void SGD(real_t learning_rate, int max_epoch, bool UI = 1);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = 1);
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std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
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real_t model_test(std::vector<real_t> x);
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void gradient_descent(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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void save(std::string file_name);
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MLPPExpReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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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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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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real_t Evaluate(std::vector<real_t> x);
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void forwardPass();
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real_t evaluatev(std::vector<real_t> x);
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std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
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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<real_t> weights;
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std::vector<real_t> initial;
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real_t bias;
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void forward_pass();
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int n;
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int k;
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std::vector<std::vector<real_t>> _input_set;
|
||||
std::vector<real_t> _output_set;
|
||||
std::vector<real_t> _y_hat;
|
||||
std::vector<real_t> _weights;
|
||||
std::vector<real_t> _initial;
|
||||
real_t _bias;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
// Regularization Params
|
||||
std::string reg;
|
||||
real_t lambda;
|
||||
real_t alpha; /* This is the controlling param for Elastic Net*/
|
||||
std::string _reg;
|
||||
real_t _lambda;
|
||||
real_t _alpha; /* This is the controlling param for Elastic Net*/
|
||||
};
|
||||
|
||||
#endif /* ExpReg_hpp */
|
||||
|
||||
@ -434,9 +434,15 @@ void MLPPTests::test_exp_reg_regression(bool ui) {
|
||||
// EXPREG REGRESSION
|
||||
std::vector<std::vector<real_t>> inputSet = { { 0, 1, 2, 3, 4 } };
|
||||
std::vector<real_t> outputSet = { 1, 2, 4, 8, 16 };
|
||||
|
||||
MLPPExpRegOld model_old(alg.transpose(inputSet), outputSet);
|
||||
model_old.SGD(0.001, 10000, ui);
|
||||
alg.printVector(model_old.modelSetTest(alg.transpose(inputSet)));
|
||||
std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPExpReg model(alg.transpose(inputSet), outputSet);
|
||||
model.SGD(0.001, 10000, ui);
|
||||
alg.printVector(model.modelSetTest(alg.transpose(inputSet)));
|
||||
model.sgd(0.001, 10000, ui);
|
||||
alg.printVector(model.model_set_test(alg.transpose(inputSet)));
|
||||
std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
|
||||
}
|
||||
void MLPPTests::test_tanh_regression(bool ui) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user