140 lines
5.3 KiB
C++
140 lines
5.3 KiB
C++
#pragma once
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/*
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* Copyright (C) 2024 Brett Terpstra
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <https://www.gnu.org/licenses/>.
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*/
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#ifndef COSC_4P80_ASSIGNMENT_2_NETWORK_H
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#define COSC_4P80_ASSIGNMENT_2_NETWORK_H
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#include <assign2/common.h>
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#include <assign2/layer.h>
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#include "blt/std/assert.h"
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namespace assign2
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{
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class network_t
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{
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public:
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template<typename WeightFunc, typename BiasFunc>
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network_t(blt::i32 input_size, blt::i32 output_size, blt::i32 layer_count, blt::i32 hidden_size, WeightFunc w, BiasFunc b)
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{
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if (layer_count > 0)
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{
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for (blt::i32 i = 0; i < layer_count; i++)
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{
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if (i == 0)
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layers.push_back(layer_t{input_size, hidden_size, w, b});
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else
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layers.push_back(layer_t{hidden_size, hidden_size, w, b});
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}
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layers.push_back(layer_t{hidden_size, output_size, w, b});
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} else
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{
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layers.push_back(layer_t{input_size, output_size, w, b});
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}
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}
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template<typename WeightFunc, typename BiasFunc, typename OutputWeightFunc, typename OutputBiasFunc>
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network_t(blt::i32 input_size, blt::i32 output_size, blt::i32 layer_count, blt::i32 hidden_size,
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WeightFunc w, BiasFunc b, OutputWeightFunc ow, OutputBiasFunc ob)
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{
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if (layer_count > 0)
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{
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for (blt::i32 i = 0; i < layer_count; i++)
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{
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if (i == 0)
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layers.push_back(layer_t{input_size, hidden_size, w, b});
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else
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layers.push_back(layer_t{hidden_size, hidden_size, w, b});
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}
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layers.push_back(layer_t{hidden_size, output_size, ow, ob});
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} else
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{
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layers.push_back(layer_t{input_size, output_size, ow, ob});
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}
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}
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explicit network_t(std::vector<layer_t> layers): layers(std::move(layers))
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{}
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network_t() = default;
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const std::vector<Scalar>& execute(const std::vector<Scalar>& input)
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{
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std::vector<blt::ref<const std::vector<Scalar>>> outputs;
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outputs.emplace_back(input);
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for (auto& v : layers)
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outputs.emplace_back(v.call(outputs.back()));
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return outputs.back();
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}
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std::pair<Scalar, Scalar> error(const std::vector<Scalar>& outputs, bool is_bad)
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{
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BLT_ASSERT(outputs.size() == 2);
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auto g = is_bad ? 0.0f : 1.0f;
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auto b = is_bad ? 1.0f : 0.0f;
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auto g_diff = outputs[0] - g;
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auto b_diff = outputs[1] - b;
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auto error = g_diff * g_diff + b_diff * b_diff;
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BLT_INFO("%f %f %f", error, g_diff, b_diff);
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return {0.5f * (error * error), error};
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}
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Scalar train_epoch(const data_file_t& example)
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{
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Scalar total_error = 0;
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Scalar total_d_error = 0;
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for (const auto& x : example.data_points)
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{
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execute(x.bins);
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std::vector<Scalar> expected{x.is_bad ? 0.0f : 1.0f, x.is_bad ? 1.0f : 0.0f};
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for (auto [i, layer] : blt::iterate(layers).enumerate().rev())
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{
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if (i == layers.size() - 1)
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{
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auto e = layer.back_prop(layers[i - 1].outputs, expected);
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total_error += e;
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} else if (i == 0)
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{
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auto e = layer.back_prop(x.bins, layers[i + 1]);
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total_error += e;
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} else
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{
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auto e = layer.back_prop(layers[i - 1].outputs, layers[i + 1]);
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total_error += e;
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}
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}
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for (auto& l : layers)
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l.update();
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}
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BLT_DEBUG("Total Errors found %f, %f", total_error, total_d_error);
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return total_error;
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}
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private:
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std::vector<layer_t> layers;
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};
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}
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#endif //COSC_4P80_ASSIGNMENT_2_NETWORK_H
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