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Fix warnings in MLP.

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Relintai 2023-02-05 01:34:14 +01:00
parent 0e48edfbaf
commit d8fb70b92d
1 changed files with 21 additions and 6 deletions
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27
mlpp/mlp/mlp.cpp
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@ -508,8 +508,16 @@ void MLPPMLP::_bind_methods() {
// ======= OLD =======
MLPPMLPOld::MLPPMLPOld(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int n_hidden, std::string reg, real_t lambda, real_t alpha) :
inputSet(inputSet), outputSet(outputSet), n_hidden(n_hidden), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
MLPPMLPOld::MLPPMLPOld(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, int p_n_hidden, std::string p_reg, real_t p_lambda, real_t p_alpha) {
inputSet = p_inputSet;
outputSet = p_outputSet;
n_hidden = p_n_hidden;
n = p_inputSet.size();
k = p_inputSet[0].size();
reg = p_reg;
lambda = p_lambda;
alpha = p_alpha;
MLPPActivation avn;
y_hat.resize(n);
@ -600,7 +608,9 @@ void MLPPMLPOld::SGD(real_t learning_rate, int max_epoch, bool UI) {
int outputIndex = distribution(generator);
real_t y_hat = Evaluate(inputSet[outputIndex]);
auto [z2, a2] = propagate(inputSet[outputIndex]);
auto propagate_result = propagate(inputSet[outputIndex]);
auto z2 = std::get<0>(propagate_result);
auto a2 = std::get<1>(propagate_result);
cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
real_t error = y_hat - outputSet[outputIndex];
@ -649,12 +659,17 @@ void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
// Creating the mini-batches
int n_mini_batch = n / mini_batch_size;
auto [inputMiniBatches, outputMiniBatches] = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto minibatches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
auto inputMiniBatches = std::get<0>(minibatches);
auto outputMiniBatches = std::get<1>(minibatches);
while (true) {
for (int i = 0; i < n_mini_batch; i++) {
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
auto [z2, a2] = propagate(inputMiniBatches[i]);
auto propagate_result = propagate(inputMiniBatches[i]);
auto z2 = std::get<0>(propagate_result);
auto a2 = std::get<1>(propagate_result);
cost_prev = Cost(y_hat, outputMiniBatches[i]);
// Calculating the errors
@ -669,7 +684,7 @@ void MLPPMLPOld::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size,
weights2 = regularization.regWeights(weights2, lambda, alpha, reg);
// Calculating the bias gradients for layer 2
real_t b_gradient = alg.sum_elements(error);
//real_t b_gradient = alg.sum_elements(error);
// Bias Updation for layer 2
bias2 -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();