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https://github.com/Relintai/pmlpp.git
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Fixed warnings in MLPPANN.
This commit is contained in:
parent
7e738f79ee
commit
14c0cede56
126
mlpp/ann/ann.cpp
126
mlpp/ann/ann.cpp
@ -15,8 +15,15 @@
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#include <iostream>
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#include <random>
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MLPPANN::MLPPANN(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet) :
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inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), lrScheduler("None"), decayConstant(0), dropRate(0) {
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MLPPANN::MLPPANN(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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n = inputSet.size();
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k = inputSet[0].size();
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lrScheduler = "None";
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decayConstant = 0;
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dropRate = 0;
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}
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MLPPANN::~MLPPANN() {
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@ -28,7 +35,7 @@ std::vector<real_t> MLPPANN::modelSetTest(std::vector<std::vector<real_t>> X) {
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network[0].input = X;
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network[0].forwardPass();
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for (int i = 1; i < network.size(); i++) {
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for (uint32_t i = 1; i < network.size(); i++) {
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network[i].input = network[i - 1].a;
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network[i].forwardPass();
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}
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@ -43,7 +50,7 @@ std::vector<real_t> MLPPANN::modelSetTest(std::vector<std::vector<real_t>> X) {
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real_t MLPPANN::modelTest(std::vector<real_t> x) {
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if (!network.empty()) {
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network[0].Test(x);
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for (int i = 1; i < network.size(); i++) {
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for (uint32_t i = 1; i < network.size(); i++) {
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network[i].Test(network[i - 1].a_test);
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}
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outputLayer->Test(network[network.size() - 1].a_test);
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@ -66,7 +73,9 @@ void MLPPANN::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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cost_prev = Cost(y_hat, outputSet);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputSet);
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auto grads = computeGradients(y_hat, outputSet);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate / n, outputWGrad);
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@ -106,7 +115,10 @@ void MLPPANN::SGD(real_t learning_rate, int max_epoch, bool UI) {
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std::vector<real_t> y_hat = modelSetTest({ inputSet[outputIndex] });
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, { outputSet[outputIndex] });
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auto grads = computeGradients(y_hat, { outputSet[outputIndex] });
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate / n, outputWGrad);
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@ -137,14 +149,21 @@ void MLPPANN::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, boo
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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while (true) {
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learning_rate = applyLearningRateScheduler(initial_learning_rate, decayConstant, epoch, dropRate);
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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cumulativeHiddenLayerWGrad = alg.scalarMultiply(learning_rate / n, cumulativeHiddenLayerWGrad);
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outputWGrad = alg.scalarMultiply(learning_rate / n, outputWGrad);
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@ -175,7 +194,10 @@ void MLPPANN::Momentum(real_t learning_rate, int max_epoch, int mini_batch_size,
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> v_hidden;
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@ -187,7 +209,9 @@ void MLPPANN::Momentum(real_t learning_rate, int max_epoch, int mini_batch_size,
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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if (!network.empty() && v_hidden.empty()) { // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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@ -232,7 +256,10 @@ void MLPPANN::Adagrad(real_t learning_rate, int max_epoch, int mini_batch_size,
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> v_hidden;
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@ -244,7 +271,9 @@ void MLPPANN::Adagrad(real_t learning_rate, int max_epoch, int mini_batch_size,
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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if (!network.empty() && v_hidden.empty()) { // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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@ -288,7 +317,10 @@ void MLPPANN::Adadelta(real_t learning_rate, int max_epoch, int mini_batch_size,
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> v_hidden;
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@ -300,7 +332,9 @@ void MLPPANN::Adadelta(real_t learning_rate, int max_epoch, int mini_batch_size,
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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if (!network.empty() && v_hidden.empty()) { // Initing our tensor
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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@ -344,7 +378,10 @@ void MLPPANN::Adam(real_t learning_rate, int max_epoch, int mini_batch_size, rea
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> m_hidden;
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@ -358,7 +395,10 @@ void MLPPANN::Adam(real_t learning_rate, int max_epoch, int mini_batch_size, rea
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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if (!network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
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m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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@ -411,7 +451,10 @@ void MLPPANN::Adamax(real_t learning_rate, int max_epoch, int mini_batch_size, r
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> m_hidden;
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@ -425,7 +468,10 @@ void MLPPANN::Adamax(real_t learning_rate, int max_epoch, int mini_batch_size, r
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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if (!network.empty() && m_hidden.empty() && u_hidden.empty()) { // Initing our tensor
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m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
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u_hidden = alg.resize(u_hidden, cumulativeHiddenLayerWGrad);
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@ -476,12 +522,14 @@ void MLPPANN::Nadam(real_t learning_rate, int max_epoch, int mini_batch_size, re
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> m_hidden;
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std::vector<std::vector<std::vector<real_t>>> v_hidden;
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std::vector<std::vector<std::vector<real_t>>> m_hidden_final;
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std::vector<real_t> m_output;
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std::vector<real_t> v_output;
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@ -491,7 +539,10 @@ void MLPPANN::Nadam(real_t learning_rate, int max_epoch, int mini_batch_size, re
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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if (!network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
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m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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@ -546,7 +597,10 @@ void MLPPANN::AMSGrad(real_t learning_rate, int max_epoch, int mini_batch_size,
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int n_mini_batch = n / mini_batch_size;
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// always evaluate the result
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// always do forward pass only ONCE at end.
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auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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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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// Initializing necessary components for Adam.
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std::vector<std::vector<std::vector<real_t>>> m_hidden;
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@ -564,7 +618,10 @@ void MLPPANN::AMSGrad(real_t learning_rate, int max_epoch, int mini_batch_size,
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std::vector<real_t> y_hat = modelSetTest(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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auto [cumulativeHiddenLayerWGrad, outputWGrad] = computeGradients(y_hat, outputMiniBatches[i]);
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auto grads = computeGradients(y_hat, outputMiniBatches[i]);
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auto cumulativeHiddenLayerWGrad = std::get<0>(grads);
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auto outputWGrad = std::get<1>(grads);
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if (!network.empty() && m_hidden.empty() && v_hidden.empty()) { // Initing our tensor
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m_hidden = alg.resize(m_hidden, cumulativeHiddenLayerWGrad);
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v_hidden = alg.resize(v_hidden, cumulativeHiddenLayerWGrad);
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@ -606,21 +663,21 @@ void MLPPANN::AMSGrad(real_t learning_rate, int max_epoch, int mini_batch_size,
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}
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real_t MLPPANN::score() {
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MLPPUtilities util;
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MLPPUtilities util;
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forwardPass();
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return util.performance(y_hat, outputSet);
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}
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void MLPPANN::save(std::string fileName) {
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MLPPUtilities util;
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MLPPUtilities util;
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if (!network.empty()) {
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util.saveParameters(fileName, network[0].weights, network[0].bias, 0, 1);
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for (int i = 1; i < network.size(); i++) {
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util.saveParameters(fileName, network[i].weights, network[i].bias, 1, i + 1);
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util.saveParameters(fileName, network[0].weights, network[0].bias, false, 1);
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for (uint32_t i = 1; i < network.size(); i++) {
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util.saveParameters(fileName, network[i].weights, network[i].bias, true, i + 1);
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}
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 1, network.size() + 1);
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, true, network.size() + 1);
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} else {
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, 0, network.size() + 1);
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util.saveParameters(fileName, outputLayer->weights, outputLayer->bias, false, network.size() + 1);
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}
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}
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@ -661,7 +718,6 @@ void MLPPANN::addLayer(int n_hidden, std::string activation, std::string weightI
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}
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void MLPPANN::addOutputLayer(std::string activation, std::string loss, std::string weightInit, std::string reg, real_t lambda, real_t alpha) {
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MLPPLinAlg alg;
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if (!network.empty()) {
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outputLayer = new MLPPOldOutputLayer(network[network.size() - 1].n_hidden, activation, loss, network[network.size() - 1].a, weightInit, reg, lambda, alpha);
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} else {
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@ -676,7 +732,7 @@ real_t MLPPANN::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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auto cost_function = outputLayer->cost_map[outputLayer->cost];
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if (!network.empty()) {
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for (int i = 0; i < network.size() - 1; i++) {
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for (uint32_t i = 0; i < network.size() - 1; i++) {
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totalRegTerm += regularization.regTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg);
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}
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}
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@ -688,7 +744,7 @@ void MLPPANN::forwardPass() {
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network[0].input = inputSet;
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network[0].forwardPass();
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for (int i = 1; i < network.size(); i++) {
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for (uint32_t i = 1; i < network.size(); i++) {
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network[i].input = network[i - 1].a;
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network[i].forwardPass();
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}
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@ -740,9 +796,9 @@ std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> M
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cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[network.size() - 1].weights, network[network.size() - 1].lambda, network[network.size() - 1].alpha, network[network.size() - 1].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
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for (int i = network.size() - 2; i >= 0; i--) {
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auto hiddenLayerAvn = network[i].activation_map[network[i].activation];
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hiddenLayerAvn = network[i].activation_map[network[i].activation];
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network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvn)(network[i].z, 1));
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std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
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hiddenLayerWGrad = alg.matmult(alg.transpose(network[i].input), network[i].delta);
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cumulativeHiddenLayerWGrad.push_back(alg.addition(hiddenLayerWGrad, regularization.regDerivTerm(network[i].weights, network[i].lambda, network[i].alpha, network[i].reg))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
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}
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}
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