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Fix crashes introduced when fixing warnings.

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Relintai 2023-02-06 12:45:15 +01:00
parent 62e4b97291
commit e594aec785
1 changed files with 9 additions and 4 deletions
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11
mlpp/wgan/wgan.cpp
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@ -598,7 +598,9 @@ void MLPPWGANOld::updateGeneratorParameters(std::vector<std::vector<std::vector<
MLPPLinAlg alg; MLPPLinAlg alg;
if (!network.empty()) { if (!network.empty()) {
for (uint32_t i = network.size() / 2; i >= 0; i--) { for (int ii = network.size() / 2; ii >= 0; ii--) {
uint32_t i = static_cast<uint32_t>(ii);
//std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl; //std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl;
//std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl; //std::cout << hiddenLayerUpdations[(network.size() - 2) - i + 1].size() << "x" << hiddenLayerUpdations[(network.size() - 2) - i + 1][0].size() << std::endl;
network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]); network[i].weights = alg.subtraction(network[i].weights, hiddenLayerUpdations[(network.size() - 2) - i + 1]);
@ -662,7 +664,8 @@ std::vector<std::vector<std::vector<real_t>>> MLPPWGANOld::computeGeneratorGradi
std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta); std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
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. 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.
for (uint32_t i = network.size() - 2; i >= 0; i--) { for (int ii = network.size() - 2; ii >= 0; ii--) {
uint32_t i = static_cast<uint32_t>(ii);
auto hiddenLayerAvnl = network[i].activation_map[network[i].activation]; auto hiddenLayerAvnl = network[i].activation_map[network[i].activation];
network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvnl)(network[i].z, 1)); network[i].delta = alg.hadamard_product(alg.matmult(network[i + 1].delta, alg.transpose(network[i + 1].weights)), (avn.*hiddenLayerAvnl)(network[i].z, 1));
std::vector<std::vector<real_t>> hiddenLayerWGradl = alg.matmult(alg.transpose(network[i].input), network[i].delta); std::vector<std::vector<real_t>> hiddenLayerWGradl = alg.matmult(alg.transpose(network[i].input), network[i].delta);
@ -677,7 +680,9 @@ void MLPPWGANOld::UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std
std::cout << "Layer " << network.size() + 1 << ": " << std::endl; std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
MLPPUtilities::UI(outputLayer->weights, outputLayer->bias); MLPPUtilities::UI(outputLayer->weights, outputLayer->bias);
if (!network.empty()) { if (!network.empty()) {
for (uint32_t i = network.size() - 1; i >= 0; i--) { for (int ii = network.size() - 1; ii >= 0; ii--) {
uint32_t i = static_cast<uint32_t>(ii);
std::cout << "Layer " << i + 1 << ": " << std::endl; std::cout << "Layer " << i + 1 << ": " << std::endl;
MLPPUtilities::UI(network[i].weights, network[i].bias); MLPPUtilities::UI(network[i].weights, network[i].bias);
} }