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implementing the xor example: losses, optimizers, datasets training etc.
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15 files changed

+242
-5
lines changed

15 files changed

+242
-5
lines changed

Diff for: ‎.gitignore

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.vscode/
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.vs/
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build/

Diff for: ‎CMakeLists.txt

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@@ -2,6 +2,9 @@ cmake_minimum_required(VERSION 3.0.0)
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project(Tipousi VERSION 0.0.1)
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set(CMAKE_CXX_STANDARD 17)
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set(CMAKE_CXX_STANDARD_REQUIRED ON)
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# Set the default build type to Release if not specified
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if(NOT CMAKE_BUILD_TYPE)
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set(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build" FORCE)
@@ -20,6 +23,8 @@ include_directories(
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${PROJECT_SOURCE_DIR}/include/layer
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${PROJECT_SOURCE_DIR}/include/graph
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${PROJECT_SOURCE_DIR}/include/base
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${PROJECT_SOURCE_DIR}/include/optimizer
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${PROJECT_SOURCE_DIR}/include/data
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)
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# Source files

Diff for: ‎CMakeSettings.json

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{
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"configurations": [
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{
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"name": "x64-Debug",
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"generator": "Ninja",
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"configurationType": "Debug",
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"inheritEnvironments": [ "msvc_x64_x64" ],
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"buildRoot": "${projectDir}\\out\\build\\${name}",
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"installRoot": "${projectDir}\\out\\install\\${name}",
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"cmakeCommandArgs": "",
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"buildCommandArgs": "",
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"ctestCommandArgs": ""
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}
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]
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}

Diff for: ‎include/data/dataset.hpp

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#pragma once
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#include <Eigen/Dense>
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#include <vector>
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namespace Tipousi
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{
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namespace Data
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{
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class Dataset
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{
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public:
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Dataset(const Eigen::MatrixXd &X, const Eigen::MatrixXd &Y);
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~Dataset() = default;
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using DataPair = std::pair<Eigen::MatrixXf, Eigen::MatrixXf>;
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class Iterator
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{
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public:
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Iterator(const Eigen::MatrixXd &X, const Eigen::MatrixXd &Y,
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size_t index);
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Iterator &operator++();
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bool operator!=(const Iterator &other) const;
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DataPair operator*() const;
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private:
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const Eigen::MatrixXd &m_X;
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const Eigen::MatrixXd &m_y;
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size_t m_index;
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};
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Iterator begin() const;
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Iterator end() const;
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private:
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Eigen::MatrixXd m_X;
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Eigen::MatrixXd m_y;
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};
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}; // namespace Data
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}; // namespace Tipousi

Diff for: ‎include/graph/sequential.hpp

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@@ -1,13 +1,14 @@
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#pragma once
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#include "graph/node.hpp"
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#include "graph/trainable.hpp"
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#include <vector>
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namespace Tipousi
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{
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namespace Graph
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{
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class Sequential
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class Sequential : public Trainable
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{
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public:
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void forward(const Eigen::MatrixXf &in, Eigen::MatrixXf &out);
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void backward();
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void train(const Data::Dataset &dataset,
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const Optimizer::OptimizerBase &optimizer,
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const Loss::LossBase &loss,
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const uint32_t n_epochs) override;
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private:
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Node *m_input_node = nullptr;
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Node *m_output_node = nullptr;

Diff for: ‎include/graph/trainable.hpp

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#pragma once
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#include "data/dataset.hpp"
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#include "loss/base.hpp"
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#include "optimizer/base.hpp"
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namespace Tipousi
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{
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namespace Graph
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{
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class Trainable
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{
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public:
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virtual void train(const Data::Dataset &dataset,
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const Optimizer::OptimizerBase &optimizer,
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const Loss::LossBase &loss,
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const uint32_t n_epochs) = 0;
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protected:
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Trainable() = default;
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virtual ~Trainable() = default;
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};
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} // namespace Graph
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} // namespace Tipousi

Diff for: ‎include/loss/base.hpp

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#pragma once
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#include <Eigen/Dense>
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namespace Tipousi
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{
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namespace Loss
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{
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class LossBase
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{
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public:
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LossBase() = default;
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virtual ~LossBase() = default;
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virtual float compute(const Eigen::MatrixXf &y,
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const Eigen::MatrixXf &y_pred) const = 0;
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virtual void grad(Eigen::MatrixXf &out_grad,
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const Eigen::MatrixXf &y,
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const Eigen::MatrixXf &y_pred) const = 0;
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};
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}; // namespace Loss
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}; // namespace Tipousi

Diff for: ‎include/loss/mse.hpp

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@@ -1,11 +1,20 @@
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#pragma once
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#include "loss/base.hpp"
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namespace Tipousi
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{
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namespace Loss
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{
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class MSE
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class MSE : public LossBase
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{
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MSE() = default;
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~MSE() = default;
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float compute(const Eigen::MatrixXf &y,
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const Eigen::MatrixXf &y_pred) const override;
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void grad(Eigen::MatrixXf &out_grad, const Eigen::MatrixXf &y,
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const Eigen::MatrixXf &y_pred) const override;
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};
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}; // namespace Loss
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}; // namespace Tipousi

Diff for: ‎include/loss/template.hpp renamed to ‎include/optimizer/base.hpp

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namespace Tipousi
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{
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namespace Loss
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namespace Optimizer
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{
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class MyTemplate
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class OptimizerBase
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{
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};
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}; // namespace Loss
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}; // namespace Optimizer
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}; // namespace Tipousi

Diff for: ‎include/optimizer/sgd.hpp

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#pragma once
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#include "optimizer/base.hpp"
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namespace Tipousi
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{
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namespace Optimizer
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{
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class SGD : public OptimizerBase
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{
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public:
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SGD();
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~SGD() = default;
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};
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}; // namespace Optimizer
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}; // namespace Tipousi

Diff for: ‎src/data/dataset.cpp

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#include "data/dataset.hpp"
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namespace Tipousi
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{
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namespace Data
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{
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Dataset::Dataset(const Eigen::MatrixXd &X, const Eigen::MatrixXd &Y)
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: m_X(X), m_y(Y)
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{
10+
}
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12+
Dataset::Iterator::Iterator(const Eigen::MatrixXd &X,
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const Eigen::MatrixXd &Y, size_t index)
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: m_X(X), m_y(Y), m_index(index)
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{
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}
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Dataset::Iterator &Dataset::Iterator::operator++()
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{
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++m_index;
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return *this;
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}
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24+
bool Dataset::Iterator::operator!=(const Dataset::Iterator &other) const
25+
{
26+
return m_index != other.m_index;
27+
}
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Dataset::DataPair Dataset::Iterator::operator*() const
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{
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Eigen::MatrixXf x = m_X.row(m_index).cast<float>();
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Eigen::MatrixXf y = m_y.row(m_index).cast<float>();
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return {x, y};
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}
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Dataset::Iterator Dataset::begin() const
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{
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return Iterator(m_X, m_y, 0);
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}
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Dataset::Iterator Dataset::end() const
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{
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return Iterator(m_X, m_y, m_X.rows());
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}
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} // namespace Data
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} // namespace Tipousi

Diff for: ‎src/graph/sequential.cpp

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#include "graph/sequential.hpp"
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#include <iostream>
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namespace Tipousi
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{
@@ -58,5 +59,29 @@ namespace Tipousi
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//
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}
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void Sequential::train(const Data::Dataset &dataset,
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const Optimizer::OptimizerBase &optimizer,
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const Loss::LossBase &loss_func,
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const uint32_t n_epochs)
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{
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for (uint32_t i{0}; i < n_epochs; i++)
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{
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float total_loss = 0.0f;
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uint32_t counter{0};
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for (const auto &[x, y] : dataset)
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{
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Eigen::MatrixXf output;
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Eigen::MatrixXf out_grad;
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forward(x, output);
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total_loss += loss_func.compute(y, output);
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loss_func.grad(out_grad, y, output);
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backward();
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counter++;
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}
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std::cout << "Epoch: " << i
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<< ", Loss: " << total_loss / counter << std::endl;
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}
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}
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} // namespace Graph
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} // namespace Tipousi

Diff for: ‎src/graph/trainable.cpp

Whitespace-only changes.

Diff for: ‎src/loss/mse.cpp

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#include "loss/mse.hpp"
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namespace Tipousi
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{
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namespace Loss
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{
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float MSE::compute(const Eigen::MatrixXf &y,
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const Eigen::MatrixXf &y_pred) const
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{
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return (y - y_pred).array().square().mean();
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}
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void MSE::grad(Eigen::MatrixXf &out_grad, const Eigen::MatrixXf &y,
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const Eigen::MatrixXf &y_pred) const
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{
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out_grad = 2.0f * (y_pred - y) / y.rows();
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}
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} // namespace Loss
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} // namespace Tipousi

Diff for: ‎src/optimizer/sgd.cpp

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#include "optimizer/sgd.hpp"
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namespace Tipousi
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{
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namespace Optimizer
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{
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SGD::SGD() {}
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} // namespace Optimizer
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} // namespace Tipousi

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