diff --git a/.idea/vcs.xml b/.idea/vcs.xml index 309514d..b619dc4 100644 --- a/.idea/vcs.xml +++ b/.idea/vcs.xml @@ -2,6 +2,18 @@ + + + + + + + + + + + + diff --git a/CMakeLists.txt b/CMakeLists.txt index 4392af7..9d661f6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,5 +1,5 @@ cmake_minimum_required(VERSION 3.25) -project(COSC-4P80-Final-Project VERSION 0.0.23) +project(COSC-4P80-Final-Project VERSION 0.0.24) option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF) option(ENABLE_UBSAN "Enable the ub sanitizer" OFF) diff --git a/graph.py b/graph.py index a275e59..18ea9e9 100644 --- a/graph.py +++ b/graph.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import pandas as pd import sys -def plot_stacked_graph(title, output, csv_file1, csv_file2, position, position2): +def plot_stacked_graph(title, output, csv_file1, csv_file2, name1, name2): # Read CSV files data1 = pd.read_csv(csv_file1, header=0) data2 = pd.read_csv(csv_file2, header=0) @@ -18,27 +18,27 @@ def plot_stacked_graph(title, output, csv_file1, csv_file2, position, position2) # Create the plot fig, ax = plt.subplots() - ax.plot(x1, y1, label=f"{csv_file1}") - ax.plot(x2, y2, label=f"{csv_file2}") + ax.plot(x1, y1, label=f"{name1}") + ax.plot(x2, y2, label=f"{name2}") - if position < 2 ** 32: - ax.axvline(x=position, color='red', linestyle='--') - ax.text(position, ax.get_ylim()[1] * 0.95, f"Feed-forward average # of epochs", color='red', fontsize=10, ha='right', va='top', backgroundcolor='white') + # if position < 2 ** 32: + # ax.axvline(x=position, color='red', linestyle='--') + # ax.text(position, ax.get_ylim()[1] * 0.95, f"Feed-forward average # of epochs", color='red', fontsize=10, ha='right', va='top', backgroundcolor='white') - if position2 < 2 ** 32: - ax.axvline(x=position2, color='red', linestyle='--') - ax.text(position2, ax.get_ylim()[1] * 0.95, f"Deep learning average # of epochs", color='red', fontsize=10, ha='right', va='top', backgroundcolor='white') + # if position2 < 2 ** 32: + # ax.axvline(x=position2, color='red', linestyle='--') + # ax.text(position2, ax.get_ylim()[1] * 0.95, f"Deep learning average # of epochs", color='red', fontsize=10, ha='right', va='top', backgroundcolor='white') ax.set_xlabel(x1_label) ax.set_ylabel(y1_label) - # ax.legend() - ax.set_title(title) + ax.legend() + ax.set_title(title, fontsize=12) plt.savefig(output) if __name__ == "__main__": if len(sys.argv) != 7: - print("Usage: python script.py <output_file> <csv_file1> <csv_file2> <position_feed_forward> <position_deep>") + print("Usage: python script.py <title> <output_file> <csv_file1> <csv_file2> <csv1_name> <csv2_name>") sys.exit(1) csv_file1 = sys.argv[3] @@ -48,4 +48,4 @@ if __name__ == "__main__": position = sys.argv[5] position2 = sys.argv[6] - plot_stacked_graph(title, output, csv_file1, csv_file2, int(position), int(position2)) \ No newline at end of file + plot_stacked_graph(title, output, csv_file1, csv_file2, position, position2) \ No newline at end of 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a/part1/forward_train_results.csv b/part1/forward_train_results.csv new file mode 100644 index 0000000..774ed56 --- /dev/null +++ b/part1/forward_train_results.csv @@ -0,0 +1,101 @@ +Epoch,Loss +0,0.699479 +1,0.496314 +2,0.410334 +3,0.358543 +4,0.322386 +5,0.295046 +6,0.27331 +7,0.255423 +8,0.240327 +9,0.227347 +10,0.216018 +11,0.206008 +12,0.197078 +13,0.189043 +14,0.181763 +15,0.175128 +16,0.169047 +17,0.163448 +18,0.158271 +19,0.153466 +20,0.148992 +21,0.144812 +22,0.140896 +23,0.137217 +24,0.133754 +25,0.130487 +26,0.127397 +27,0.124471 +28,0.121694 +29,0.119055 +30,0.116543 +31,0.114149 +32,0.111865 +33,0.109682 +34,0.107593 +35,0.105593 +36,0.103676 +37,0.101836 +38,0.10007 +39,0.0983709 +40,0.0967364 +41,0.0951626 +42,0.0936461 +43,0.0921836 +44,0.0907725 +45,0.0894097 +46,0.088093 +47,0.08682 +48,0.0855883 +49,0.0843962 +50,0.0832418 +51,0.0821232 +52,0.0810387 +53,0.0799869 +54,0.0789662 +55,0.0779754 +56,0.077013 +57,0.0760781 +58,0.0751694 +59,0.0742859 +60,0.0734264 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199 & 98\\ + Deep Learning & 9898 & 101 & 99\\ + \end{tabular} +\end{figure} diff --git a/src/MNIST.cpp b/src/MNIST.cpp index 8fb81b8..374a0dd 100644 --- a/src/MNIST.cpp +++ b/src/MNIST.cpp @@ -684,6 +684,34 @@ namespace fp return run_network_tests<net_type_ff>(path, "feed_forward", runs, restore); } + auto run_deep_learning_tests_v2(const std::string& path, const blt::i32 runs, const bool restore) + { + using namespace dlib; + using net_type_dl = loss_multiclass_log< + fc<10, + relu<fc<16, + relu<fc<16, + max_pool<2, 2, 2, 2, relu<con<16, 5, 5, 1, 1, + max_pool<2, 2, 2, 2, relu<con<6, 5, 5, 1, 1, + input<matrix<blt::u8>>>>>>>>>>>>>>; + BLT_TRACE("Running deep learning tests"); + return run_network_tests<net_type_dl>(path, "deep_learning", runs, restore); + } + + auto run_feed_forward_tests_v2(const std::string& path, const blt::i32 runs, const bool restore) + { + using namespace dlib; + + using net_type_ff = loss_multiclass_log< + fc<10, + relu<fc<16, + relu<fc<16, + input<matrix<blt::u8>>>>>>>>; + + BLT_TRACE("Running feed forward tests"); + return run_network_tests<net_type_ff>(path, "feed_forward", runs, restore); + } + void run_mnist(const int argc, const char** argv) { binary_directory = std::filesystem::current_path(); @@ -756,8 +784,10 @@ namespace fp const auto restore = args.get<bool>("restore"); 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 [deep_stats, deep_tests] = run_deep_learning_tests(path, runs, restore); + auto [forward_stats, forward_tests] = run_feed_forward_tests_v2(path, runs, restore); + auto [deep_stats, deep_tests] = run_deep_learning_tests_v2(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();