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MLPPTanhReg initial cleanup.
This commit is contained in:
parent
e8d0b13eed
commit
47155163b1
@ -15,47 +15,91 @@
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#include <iostream>
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#include <random>
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MLPPTanhReg::MLPPTanhReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t alpha) :
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inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
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y_hat.resize(n);
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weights = MLPPUtilities::weightInitialization(k);
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bias = MLPPUtilities::biasInitialization();
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/*
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Ref<MLPPMatrix> MLPPTanhReg::get_input_set() {
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return _input_set;
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}
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void MLPPTanhReg::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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_initialized = false;
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}
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std::vector<real_t> MLPPTanhReg::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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Ref<MLPPMatrix> MLPPTanhReg::get_output_set() {
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return _output_set;
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}
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void MLPPTanhReg::set_output_set(const Ref<MLPPMatrix> &val) {
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_output_set = val;
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_initialized = false;
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}
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real_t MLPPTanhReg::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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MLPPReg::RegularizationType MLPPTanhReg::get_reg() {
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return _reg;
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}
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void MLPPTanhReg::set_reg(const MLPPReg::RegularizationType val) {
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_reg = val;
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_initialized = false;
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}
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void MLPPTanhReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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real_t MLPPTanhReg::get_lambda() {
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return _lambda;
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}
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void MLPPTanhReg::set_lambda(const real_t val) {
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_lambda = val;
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_initialized = false;
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}
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real_t MLPPTanhReg::get_alpha() {
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return _alpha;
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}
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void MLPPTanhReg::set_alpha(const real_t val) {
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_alpha = val;
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_initialized = false;
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}
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*/
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std::vector<real_t> MLPPTanhReg::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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real_t MLPPTanhReg::model_test(std::vector<real_t> x) {
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return evaluatev(x);
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}
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void MLPPTanhReg::gradient_descent(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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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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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), alg.hadamard_product(error, avn.tanh(z, 1)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(_input_set), alg.hadamard_product(error, avn.tanh(_z, 1)))));
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//_reg
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_weights = regularization.regWeights(_weights, _lambda, _alpha, "None");
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.tanh(z, 1))) / n;
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_bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.tanh(_z, 1))) / _n;
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forwardPass();
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forward_pass();
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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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MLPPUtilities::UI(weights, bias);
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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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@ -64,35 +108,38 @@ void MLPPTanhReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI)
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}
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}
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void MLPPTanhReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPTanhReg::sgd(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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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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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[outputIndex]);
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cost_prev = cost({ _y_hat }, { _output_set[outputIndex] });
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real_t error = y_hat - outputSet[outputIndex];
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real_t error = y_hat - _output_set[outputIndex];
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// Weight Updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error * (1 - y_hat * y_hat), inputSet[outputIndex]));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate * error * (1 - y_hat * y_hat), _input_set[outputIndex]));
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//_reg
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_weights = regularization.regWeights(_weights, _lambda, _alpha, "None");
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// Bias updation
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bias -= learning_rate * error * (1 - y_hat * y_hat);
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_bias -= learning_rate * error * (1 - y_hat * y_hat);
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y_hat = Evaluate({ inputSet[outputIndex] });
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y_hat = evaluatev(_input_set[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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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ _y_hat }, { _output_set[outputIndex] }));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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@ -100,10 +147,11 @@ void MLPPTanhReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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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 MLPPTanhReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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void MLPPTanhReg::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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@ -112,85 +160,163 @@ void MLPPTanhReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
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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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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 inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = 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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std::vector<real_t> z = propagate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
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std::vector<real_t> z = propagatem(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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// Calculating the weight gradients
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), alg.hadamard_product(error, avn.tanh(z, 1)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), alg.hadamard_product(error, avn.tanh(z, 1)))));
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//_reg
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_weights = regularization.regWeights(_weights, _lambda, _alpha, "None");
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.tanh(z, 1))) / n;
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_bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.tanh(_z, true))) / _n;
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forwardPass();
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forward_pass();
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y_hat = Evaluate(inputMiniBatches[i]);
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y_hat = evaluatem(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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MLPPUtilities::UI(weights, bias);
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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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}
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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 MLPPTanhReg::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 MLPPTanhReg::save(std::string fileName) {
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void MLPPTanhReg::save(std::string file_name) {
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MLPPUtilities util;
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util.saveParameters(fileName, weights, bias);
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util.saveParameters(file_name, _weights, _bias);
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}
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real_t MLPPTanhReg::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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bool MLPPTanhReg::is_initialized() {
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return _initialized;
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}
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void MLPPTanhReg::initialize() {
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if (_initialized) {
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return;
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}
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//ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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_initialized = true;
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}
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MLPPTanhReg::MLPPTanhReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, MLPPReg::RegularizationType 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 = _input_set.size();
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_k = _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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_bias = MLPPUtilities::biasInitialization();
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}
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MLPPTanhReg::MLPPTanhReg() {
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}
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MLPPTanhReg::~MLPPTanhReg() {
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}
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real_t MLPPTanhReg::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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//_reg
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return cost.MSE(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, "None");
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}
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std::vector<real_t> MLPPTanhReg::Evaluate(std::vector<std::vector<real_t>> X) {
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real_t MLPPTanhReg::evaluatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.tanh(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
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return avn.tanh(alg.dot(_weights, x) + _bias);
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}
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std::vector<real_t> MLPPTanhReg::propagate(std::vector<std::vector<real_t>> X) {
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real_t MLPPTanhReg::propagatev(std::vector<real_t> x) {
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MLPPLinAlg alg;
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return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
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return alg.dot(_weights, x) + _bias;
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}
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real_t MLPPTanhReg::Evaluate(std::vector<real_t> x) {
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std::vector<real_t> MLPPTanhReg::evaluatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.tanh(alg.dot(weights, x) + bias);
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return avn.tanh(alg.scalarAdd(_bias, alg.mat_vec_mult(X, _weights)));
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}
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real_t MLPPTanhReg::propagate(std::vector<real_t> x) {
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std::vector<real_t> MLPPTanhReg::propagatem(std::vector<std::vector<real_t>> X) {
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MLPPLinAlg alg;
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return alg.dot(weights, x) + bias;
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return alg.scalarAdd(_bias, alg.mat_vec_mult(X, _weights));
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}
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// Tanh ( wTx + b )
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void MLPPTanhReg::forwardPass() {
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void MLPPTanhReg::forward_pass() {
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MLPPActivation avn;
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z = propagate(inputSet);
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y_hat = avn.tanh(z);
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_z = propagatem(_input_set);
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_y_hat = avn.tanh(_z);
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}
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void MLPPTanhReg::_bind_methods() {
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/*
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPTanhReg::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPTanhReg::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPTanhReg::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPTanhReg::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_reg"), &MLPPTanhReg::get_reg);
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ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPTanhReg::set_reg);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
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ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPTanhReg::get_lambda);
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ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPTanhReg::set_lambda);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
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ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPTanhReg::get_alpha);
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ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPTanhReg::set_alpha);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPTanhReg::model_test);
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPTanhReg::model_set_test);
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ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPTanhReg::gradient_descent, false);
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ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPTanhReg::sgd, false);
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ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPTanhReg::mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPTanhReg::score);
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ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPTanhReg::save);
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ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPTanhReg::is_initialized);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPTanhReg::initialize);
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*/
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}
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@ -10,46 +10,85 @@
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include "../regularization/reg.h"
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#include <string>
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#include <vector>
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class MLPPTanhReg {
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class MLPPTanhReg : public Reference {
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GDCLASS(MLPPTanhReg, Reference);
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public:
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MLPPTanhReg(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 = 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);
|
||||
/*
|
||||
Ref<MLPPMatrix> get_input_set();
|
||||
void set_input_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPMatrix> get_output_set();
|
||||
void set_output_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
MLPPReg::RegularizationType get_reg();
|
||||
void set_reg(const MLPPReg::RegularizationType val);
|
||||
|
||||
real_t get_lambda();
|
||||
void set_lambda(const real_t val);
|
||||
|
||||
real_t get_alpha();
|
||||
void set_alpha(const real_t val);
|
||||
*/
|
||||
|
||||
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);
|
||||
void save(std::string file_name);
|
||||
|
||||
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
|
||||
std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
|
||||
real_t Evaluate(std::vector<real_t> x);
|
||||
real_t propagate(std::vector<real_t> x);
|
||||
void forwardPass();
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<real_t> outputSet;
|
||||
std::vector<real_t> z;
|
||||
std::vector<real_t> y_hat;
|
||||
std::vector<real_t> weights;
|
||||
real_t bias;
|
||||
MLPPTanhReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
|
||||
|
||||
int n;
|
||||
int k;
|
||||
MLPPTanhReg();
|
||||
~MLPPTanhReg();
|
||||
|
||||
// UI Portion
|
||||
void UI(int epoch, real_t cost_prev);
|
||||
protected:
|
||||
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
|
||||
real_t evaluatev(std::vector<real_t> x);
|
||||
real_t propagatev(std::vector<real_t> x);
|
||||
|
||||
std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
|
||||
std::vector<real_t> propagatem(std::vector<std::vector<real_t>> X);
|
||||
|
||||
void forward_pass();
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
std::vector<std::vector<real_t>> _input_set;
|
||||
std::vector<real_t> _output_set;
|
||||
std::vector<real_t> _z;
|
||||
std::vector<real_t> _y_hat;
|
||||
std::vector<real_t> _weights;
|
||||
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*/
|
||||
MLPPReg::RegularizationType _reg;
|
||||
real_t _lambda;
|
||||
real_t _alpha; /* This is the controlling param for Elastic Net*/
|
||||
|
||||
bool _initialized;
|
||||
};
|
||||
|
||||
#endif /* TanhReg_hpp */
|
||||
|
||||
Loading…
Reference in New Issue
Block a user