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COSC-4P80-Final-Project
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Brett 2025-01-08 18:02:41 -05:00
parent 818c1151da
commit cacc94d937
2 changed files with 48 additions and 52 deletions
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2
CMakeLists.txt
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@ -1,5 +1,5 @@
cmake_minimum_required(VERSION 3.25)
project(COSC-4P80-Final-Project VERSION 0.0.11)
project(COSC-4P80-Final-Project VERSION 0.0.12)
option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
option(ENABLE_UBSAN "Enable the ub sanitizer" OFF)

98
src/MNIST.cpp
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@ -580,15 +580,22 @@ namespace fp
void run_mnist(const int argc, const char** argv)
{
binary_directory = std::filesystem::current_path();
blt::size_t pos = 0;
if (!blt::string::ends_with(binary_directory, '/'))
{
pos = binary_directory.find_last_of('/') - 1;
binary_directory += '/';
python_dual_stacked_graph_program = binary_directory + "../graph.py";
}
else
pos = binary_directory.substr(0, binary_directory.size() - 1).find_last_of('/') - 1;
python_dual_stacked_graph_program = binary_directory.substr(0, pos) + "/graph.py";
BLT_TRACE(binary_directory);
BLT_TRACE(python_dual_stacked_graph_program);
BLT_TRACE("Running with batch size %d", batch_size);
BLT_TRACE("Installing Signal Handlers");
if (std::signal(SIGINT, [](int){
if (std::signal(SIGINT, [](int)
{
BLT_TRACE("Stopping current training");
break_flag = true;
}) == SIG_ERR)
@ -623,68 +630,57 @@ namespace fp
parser.addArgument(
blt::arg_builder{"-p", "--python"}.setHelp("Only run the python scripts").setAction(blt::arg_action_t::STORE_TRUE).setDefault(false).
build());
parser.addArgument(blt::arg_builder{"type"}.setDefault("all").setHelp("Type of network to run [ff, dl, default: all]").build());
parser.addArgument(
blt::arg_builder{"network"}.setDefault(std::to_string(blt::system::getCurrentTimeMilliseconds())).setHelp("location of network files").
build());
auto args = parser.parse_args(argc, argv);
const auto type = blt::string::toLowerCase(args.get<std::string>("type"));
const auto runs = std::stoi(args.get<std::string>("runs"));
const auto restore = args.get<bool>("restore");
const auto path = binary_directory + std::to_string(blt::system::getCurrentTimeMilliseconds());
auto path = binary_directory + args.get<std::string>("network");
auto [deep_stats, deep_tests] = run_deep_learning_tests(path, runs, restore);
auto [forward_stats, forward_tests] = run_feed_forward_tests(path, runs, restore);
auto average_forward_size = forward_stats.average_size();
auto average_deep_size = deep_stats.average_size();
if (type == "all")
{
auto [deep_stats, deep_tests] = run_deep_learning_tests(path, runs, restore);
auto [forward_stats, forward_tests] = run_feed_forward_tests(path, runs, restore);
std::ofstream test_results_f{path + "/test_results_table.txt"};
test_results_f << "\\begin{figure}" << std::endl;
test_results_f << "\t\\begin{tabular}{|c|c|c|c|}" << std::endl;
test_results_f << "\t\t\\hline" << std::endl;
test_results_f << "\t\tTest & Correct & Incorrect & Accuracy (\\%) \\\\" << std::endl;
test_results_f << "\t\t\\hline" << std::endl;
auto test_accuracy = forward_tests.hits / static_cast<double>(forward_tests.hits + forward_tests.misses) * 100;
test_results_f << "\t\tFeed-Forward & " << forward_tests.hits << " & " << forward_tests.misses << " & " << std::setprecision(2) <<
test_accuracy << "\\\\" << std::endl;
test_accuracy = deep_tests.hits / static_cast<double>(deep_tests.hits + deep_tests.misses) * 100;
test_results_f << "\t\tDeep Learning & " << deep_tests.hits << " & " << deep_tests.misses << " & " << std::setprecision(2) <<
test_accuracy << "\\\\" << std::endl;
test_results_f << "\t\\end{tabular}" << std::endl;
test_results_f << "\\end{figure}" << std::endl;
auto average_forward_size = forward_stats.average_size();
auto average_deep_size = deep_stats.average_size();
const auto [forward_epoch_stats] = forward_stats.average_stats();
std::ofstream train_forward{path + "/forward_train_results.csv"};
train_forward << "Epoch,Loss" << std::endl;
for (const auto& [i, v] : blt::enumerate(forward_epoch_stats))
train_forward << i << ',' << v.average_loss << std::endl;
{
std::ofstream test_results_f{path + "/test_results_table.txt"};
test_results_f << "\\begin{figure}" << std::endl;
test_results_f << "\t\\begin{tabular}{|c|c|c|c|}" << std::endl;
test_results_f << "\t\t\\hline" << std::endl;
test_results_f << "\t\tTest & Correct & Incorrect & Accuracy (\\%) \\\\" << std::endl;
test_results_f << "\t\t\\hline" << std::endl;
auto test_accuracy = forward_tests.hits / static_cast<double>(forward_tests.hits + forward_tests.misses) * 100;
test_results_f << "\t\tFeed-Forward & " << forward_tests.hits << " & " << forward_tests.misses << " & " << std::setprecision(2) <<
test_accuracy << "\\\\" << std::endl;
test_accuracy = deep_tests.hits / static_cast<double>(deep_tests.hits + deep_tests.misses) * 100;
test_results_f << "\t\tDeep Learning & " << deep_tests.hits << " & " << deep_tests.misses << " & " << std::setprecision(2) <<
test_accuracy << "\\\\" << std::endl;
test_results_f << "\t\\end{tabular}" << std::endl;
test_results_f << "\\end{figure}" << std::endl;
const auto [deep_epoch_stats] = deep_stats.average_stats();
std::ofstream train_deep{path + "/deep_train_results.csv"};
train_deep << "Epoch,Loss" << std::endl;
for (const auto& [i, v] : blt::enumerate(deep_epoch_stats))
train_deep << i << ',' << v.average_loss << std::endl;
const auto [forward_epoch_stats] = forward_stats.average_stats();
std::ofstream train_forward{path + "/forward_train_results.csv"};
train_forward << "Epoch,Loss" << std::endl;
for (const auto& [i, v] : blt::enumerate(forward_epoch_stats))
train_forward << i << ',' << v.average_loss << std::endl;
const auto [deep_epoch_stats] = deep_stats.average_stats();
std::ofstream train_deep{path + "/deep_train_results.csv"};
train_deep << "Epoch,Loss" << std::endl;
for (const auto& [i, v] : blt::enumerate(deep_epoch_stats))
train_deep << i << ',' << v.average_loss << std::endl;
std::ofstream average_epochs{path + "/average_epochs.txt"};
average_epochs << average_forward_size << "," << average_deep_size << std::endl;
}
run_python_line_graph("Feed-Forward vs Deep Learning, Average Loss over Epochs", "epochs.png", path + "/forward_train_results.csv",
path + "/deep_train_results.csv", average_forward_size, average_deep_size);
}
else if (type == "ff")
{
run_feed_forward_tests(path, runs, restore);
}
else if (type == "df")
{
run_deep_learning_tests(path, runs, restore);
std::ofstream average_epochs{path + "/average_epochs.txt"};
average_epochs << average_forward_size << "," << average_deep_size << std::endl;
}
run_python_line_graph("Feed-Forward vs Deep Learning, Average Loss over Epochs", "epochs.png", path + "/forward_train_results.csv",
path + "/deep_train_results.csv", average_forward_size, average_deep_size);
// net_type_dl test_net;
// const auto stats = train_network("dl_nn", test_net);
// std::ofstream out_file{"dl_nn.csv"};