131 lines
5.2 KiB
C++
131 lines
5.2 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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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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input_size(input_size), output_size(output_size), hidden_count(layer_count), hidden_size(hidden_size)
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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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input_size(input_size), output_size(output_size), hidden_count(layer_count), hidden_size(hidden_size)
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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):
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input_size(layers.begin()->get_in_size()), output_size(layers.end()->get_out_size()),
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hidden_count(static_cast<blt::i32>(layers.size()) - 1), hidden_size(layers.end()->get_in_size()), layers(std::move(layers))
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{}
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network_t() = default;
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template<typename ActFunc, typename ActFuncOut>
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std::vector<Scalar> execute(const std::vector<Scalar>& input, ActFunc func, ActFuncOut outFunc)
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{
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std::vector<Scalar> previous_output;
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std::vector<Scalar> current_output;
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for (auto [i, v] : blt::enumerate(layers))
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{
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previous_output = current_output;
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if (i == 0)
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current_output = v.call(input, func);
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else if (i == layers.size() - 1)
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current_output = v.call(previous_output, outFunc);
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else
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current_output = v.call(previous_output, func);
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}
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return current_output;
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}
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Scalar train(const data_file_t& example)
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{
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const Scalar learn_rate = 0.1;
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Scalar total_error = 0;
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for (const auto& x : example.data_points)
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{
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auto o = execute(x.bins, sigmoid_function{}, sigmoid_function{});
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auto y = x.is_bad ? 1.0f : 0.0f;
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Scalar is_bad = 0;
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if (o[0] >= 1)
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is_bad = 0;
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else if (o[1] >= 1)
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is_bad = 1;
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auto error = y - is_bad;
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if (o[0] >= 1 && o[1] >= 1)
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error += 1;
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total_error += error;
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}
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return total_error;
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}
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private:
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blt::i32 input_size, output_size, hidden_count, hidden_size;
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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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