#pragma once /* * Copyright (C) 2024 Brett Terpstra * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef COSC_4P80_ASSIGNMENT_2_NETWORK_H #define COSC_4P80_ASSIGNMENT_2_NETWORK_H #include #include #include "blt/std/assert.h" #include "global_magic.h" namespace assign2 { class network_t { public: template network_t(blt::i32 input_size, blt::i32 output_size, blt::i32 layer_count, blt::i32 hidden_size, WeightFunc w, BiasFunc b) { if (layer_count > 0) { for (blt::i32 i = 0; i < layer_count; i++) { if (i == 0) layers.push_back(std::make_unique(input_size, hidden_size, w, b)); else layers.push_back(std::make_unique(hidden_size, hidden_size, w, b)); } layers.push_back(std::make_unique(hidden_size, output_size, w, b)); } else { layers.push_back(std::make_unique(input_size, output_size, w, b)); } } template network_t(blt::i32 input_size, blt::i32 output_size, blt::i32 layer_count, blt::i32 hidden_size, WeightFunc w, BiasFunc b, OutputWeightFunc ow, OutputBiasFunc ob) { if (layer_count > 0) { for (blt::i32 i = 0; i < layer_count; i++) { if (i == 0) layers.push_back(std::make_unique(input_size, hidden_size, w, b)); else layers.push_back(std::make_unique(hidden_size, hidden_size, w, b)); } layers.push_back(std::make_unique(hidden_size, output_size, ow, ob)); } else { layers.push_back(std::make_unique(input_size, output_size, ow, ob)); } } explicit network_t(std::vector> layers): layers(std::move(layers)) {} network_t() = default; const std::vector& execute(const std::vector& input) { std::vector>> outputs; outputs.emplace_back(input); for (auto& v : layers) outputs.emplace_back(v->call(outputs.back())); return outputs.back(); } std::pair train_epoch(const data_file_t& example) { Scalar total_error = 0; Scalar total_d_error = 0; for (const auto& x : example.data_points) { execute(x.bins); std::vector expected{x.is_bad ? 0.0f : 1.0f, x.is_bad ? 1.0f : 0.0f}; for (auto [i, layer] : blt::iterate(layers).enumerate().rev()) { if (i == layers.size() - 1) { auto e = layer->back_prop(layers[i - 1]->outputs, expected); // layer->update(); total_error += e.first; total_d_error += e.second; } else if (i == 0) { auto e = layer->back_prop(x.bins, *layers[i + 1]); // layer->update(); total_error += e.first; total_d_error += e.second; } else { auto e = layer->back_prop(layers[i - 1]->outputs, *layers[i + 1]); // layer->update(); total_error += e.first; total_d_error += e.second; } } for (auto& l : layers) l->update(); } // errors_over_time.push_back(total_error); // BLT_DEBUG("Total Errors found %f, %f", total_error, total_d_error); return {total_error, total_d_error}; } #ifdef BLT_USE_GRAPHICS void render() const { for (auto& l : layers) l->render(); } #endif private: std::vector> layers; }; } #endif //COSC_4P80_ASSIGNMENT_2_NETWORK_H