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#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_LAYER_H
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#define COSC_4P80_ASSIGNMENT_2_LAYER_H
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#include <blt/std/types.h>
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#include <assign2/initializers.h>
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#include "blt/iterator/zip.h"
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#include "blt/iterator/iterator.h"
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namespace assign2
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{
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class neuron_t
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{
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friend layer_t;
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public:
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// empty neuron for loading from a stream
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explicit neuron_t(weight_view weights): weights(weights)
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{}
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// neuron with bias
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explicit neuron_t(weight_view weights, Scalar bias): bias(bias), weights(weights)
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{}
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Scalar activate(const Scalar* inputs, function_t* act_func)
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{
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z = bias;
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for (auto [x, w] : blt::zip_iterator_container({inputs, inputs + weights.size()}, {weights.begin(), weights.end()}))
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z += x * w;
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a = act_func->call(z);
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return a;
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}
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template<typename OStream>
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OStream& serialize(OStream& stream)
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{
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stream << bias;
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for (auto d : weights)
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stream << d;
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}
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template<typename IStream>
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IStream& deserialize(IStream& stream)
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{
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for (auto& d : blt::iterate(weights).rev())
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stream >> d;
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stream >> bias;
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}
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void debug() const
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{
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std::cout << bias << " ";
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}
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private:
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float z = 0;
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float a = 0;
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float bias = 0;
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float error = 0;
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weight_view weights;
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};
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class layer_t
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{
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public:
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template<typename WeightFunc, typename BiasFunc>
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layer_t(const blt::i32 in, const blt::i32 out, function_t* act_func, WeightFunc w, BiasFunc b):
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in_size(in), out_size(out), act_func(act_func)
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{
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neurons.reserve(out_size);
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for (blt::i32 i = 0; i < out_size; i++)
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{
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auto weight = weights.allocate_view(in_size);
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for (auto& v : weight)
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v = w(i);
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neurons.push_back(neuron_t{weight, b(i)});
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}
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}
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std::vector<Scalar> call(const std::vector<Scalar>& in)
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{
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std::vector<Scalar> out;
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out.reserve(out_size);
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#if BLT_DEBUG_LEVEL > 0
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if (in.size() != in_size)
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throw std::runtime_exception("Input vector doesn't match expected input size!");
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#endif
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for (auto& n : neurons)
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out.push_back(n.activate(in.data(), act_func));
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return out;
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}
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Scalar back_prop(const std::vector<Scalar>& prev_layer_output, Scalar error, const layer_t& next_layer, bool is_output)
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{
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std::vector<Scalar> dw;
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// δ(h)
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if (is_output)
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{
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// assign error to output layer
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for (auto& n : neurons)
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n.error = act_func->derivative(n.z) * error; // f'act(net(h)) * (error)
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} else
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{
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// first calculate and assign input layer error
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std::vector<Scalar> next_error;
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next_error.resize(next_layer.neurons.size());
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for (const auto& [i, w] : blt::enumerate(next_layer.neurons))
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{
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for (auto wv : w.weights)
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next_error[i] += w.error * wv;
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// needed?
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next_error[i] /= static_cast<Scalar>(w.weights.size());
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}
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for (auto& n : neurons)
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{
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n.error = act_func->derivative(n.z);
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}
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}
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for (const auto& v : prev_layer_output)
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{
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}
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return error_at_current_layer;
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}
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template<typename OStream>
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OStream& serialize(OStream& stream)
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{
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for (auto d : neurons)
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stream << d;
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}
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template<typename IStream>
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IStream& deserialize(IStream& stream)
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{
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for (auto& d : blt::iterate(neurons).rev())
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stream >> d;
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}
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[[nodiscard]] inline blt::i32 get_in_size() const
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{
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return in_size;
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}
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[[nodiscard]] inline blt::i32 get_out_size() const
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{
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return out_size;
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}
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void debug() const
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{
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std::cout << "Bias: ";
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for (auto& v : neurons)
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v.debug();
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std::cout << std::endl;
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weights.debug();
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}
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private:
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const blt::i32 in_size, out_size;
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weight_t weights;
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function_t* act_func;
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std::vector<neuron_t> neurons;
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};
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}
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#endif //COSC_4P80_ASSIGNMENT_2_LAYER_H
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