From 9a8275244b8ade3a0e6537419b163d09d8b90cfd Mon Sep 17 00:00:00 2001 From: Andreas Karatzas Date: Mon, 2 May 2022 01:49:20 -0500 Subject: [PATCH] Project re-structuring --- .gitattributes | 63 -- .gitignore | 340 --------- LICENSE | 674 ----------------- README.md | 44 +- build/README.md | 3 + data/README.md | 5 + docs/sample-run.PNG | Bin 0 -> 524671 bytes expected-output.PNG | Bin 68678 -> 0 bytes lab4-neural-network.pdf | Bin 534245 -> 0 bytes .../Activation.hpp => lib/activation.hpp | 50 +- neural-network/Common.hpp => lib/common.hpp | 98 +-- neural-network/Dataset.hpp => lib/dataset.hpp | 122 ++-- neural-network/Driver.hpp => lib/driver.hpp | 26 +- .../Interface.hpp => lib/interface.hpp | 182 ++--- neural-network/Neural.hpp => lib/neural.hpp | 167 +++-- neural-network/Parser.hpp => lib/parser.hpp | 34 +- neural-network/Driver.cpp => main.cpp | 90 +-- makefile | 16 +- neural-network.sln | 31 - neural-network/neural-network.vcxproj | 229 ------ neural-network/neural-network.vcxproj.filters | 78 -- .../Accuracy.cpp => src/accuracy.cpp | 66 +- .../Activation.cpp => src/activation.cpp | 242 +++---- neural-network/Dataset.cpp => src/dataset.cpp | 484 ++++++------- neural-network/Export.cpp => src/export.cpp | 78 +- neural-network/Fit.cpp => src/fit.cpp | 144 ++-- neural-network/Forward.cpp => src/forward.cpp | 106 +-- .../Interface.cpp => src/interface.cpp | 678 +++++++++--------- neural-network/Loss.cpp => src/loss.cpp | 54 +- .../Optimize.cpp => src/optimize.cpp | 182 ++--- neural-network/Parser.cpp => src/parser.cpp | 112 +-- .../Utilities.cpp => src/utilities.cpp | 428 +++++------ 32 files changed, 1712 insertions(+), 3114 deletions(-) delete mode 100644 .gitattributes delete mode 100644 .gitignore delete mode 100644 LICENSE create mode 100644 build/README.md create mode 100644 data/README.md create mode 100644 docs/sample-run.PNG delete mode 100644 expected-output.PNG delete mode 100644 lab4-neural-network.pdf rename neural-network/Activation.hpp => lib/activation.hpp (91%) rename neural-network/Common.hpp => lib/common.hpp (87%) rename neural-network/Dataset.hpp => lib/dataset.hpp (93%) rename neural-network/Driver.hpp => lib/driver.hpp (58%) rename neural-network/Interface.hpp => lib/interface.hpp (94%) rename neural-network/Neural.hpp => lib/neural.hpp (85%) rename neural-network/Parser.hpp => lib/parser.hpp (86%) rename neural-network/Driver.cpp => main.cpp (97%) delete mode 100644 neural-network.sln delete mode 100644 neural-network/neural-network.vcxproj delete mode 100644 neural-network/neural-network.vcxproj.filters rename neural-network/Accuracy.cpp => src/accuracy.cpp (95%) rename neural-network/Activation.cpp => src/activation.cpp (95%) rename neural-network/Dataset.cpp => src/dataset.cpp (96%) rename neural-network/Export.cpp => src/export.cpp (96%) rename neural-network/Fit.cpp => src/fit.cpp (97%) rename neural-network/Forward.cpp => src/forward.cpp (97%) rename neural-network/Interface.cpp => src/interface.cpp (96%) rename neural-network/Loss.cpp => src/loss.cpp (94%) rename neural-network/Optimize.cpp => src/optimize.cpp (95%) rename neural-network/Parser.cpp => src/parser.cpp (96%) rename neural-network/Utilities.cpp => src/utilities.cpp (96%) diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 1ff0c42..0000000 --- a/.gitattributes +++ /dev/null @@ -1,63 +0,0 @@ -############################################################################### -# Set default behavior to automatically normalize line endings. -############################################################################### -* text=auto - -############################################################################### -# Set default behavior for command prompt diff. -# -# This is need for earlier builds of msysgit that does not have it on by -# default for csharp files. -# Note: This is only used by command line -############################################################################### -#*.cs diff=csharp - -############################################################################### -# Set the merge driver for project and solution files -# -# Merging from the command prompt will add diff markers to the files if there -# are conflicts (Merging from VS is not affected by the settings below, in VS -# the diff markers are never inserted). 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If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. diff --git a/README.md b/README.md index 4d67343..737d058 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,36 @@ -# Neural Network +# Feed Forward Neural Network in C++ 17 In this repository, there is a simple implementation of a feed forward multi-layer Neural Network from scratch. The project specifications are listed below: * Programming Language: *C++ 17* -* Optimization framework: *OpenMP 5.1* -This implementation runs on the system's __CPU__ in parallel. The model was tested using the [Fashion MNIST](https://www.kaggle.com/zalando-research/fashionmnist/data) dataset. +This implementation runs on the system's __CPU__ in serial mode. The model was tested using the [Fashion MNIST](https://www.kaggle.com/zalando-research/fashionmnist/data) dataset. + +## Prerequisites + +On Windows OS, the installation used `Intel OneAPI`, which is available for all University Students. On Linux, the installation is straight forward. There is a `makefile` that can be used to build the project on any Linux distribution. ## Installation * Open a terminal -* Clone the repository `git clone https://github.com/andreasceid/neural-network.git` -* Change directory using `cd neural-network/` -* Compile the project using `make` -* Create the dataset directory using `mkdir data` +* Clone the repository `git clone https://github.com/AndreasKaratzas/ece572.git` +* Compile the project using `make` +* Create the dataset directory using `mkdir data` * Extract the CSV files found on [Kaggle](https://www.kaggle.com/zalando-research/fashionmnist/data) in `data` directory created before * Execute the project: - * Change directory using `cd build` - * Use `./Neural-Network -i -h [-h ...] -o ` + * Change directory using `cd build` + * Use `nn.out -i -h [-h ...] -o ` + + For example `nn.out -i 784 -h 150 -h 100 -h 50 -o 10` - For example `./Neural-Network -i 784 -h 150 -h 100 -h 50 -o 10` +To compile using the Intel Compiler in a Windows environment, use: +```powershell +icx main.cpp src/accuracy.cpp src/activation.cpp src/dataset.cpp src/export.cpp src/fit.cpp src/forward.cpp src/interface.cpp src/loss.cpp src/optimize.cpp src/parser.cpp src/utilities.cpp /Ilib /Qopenmp /Qunroll /Qipo /O3 /Ot /Ob2 /Oi /GA /fp:precise /QxHost /Qstd:c++17 /Fenn.exe +``` -To compile using the Intel Compiler in a Windows environment, use: `icl Accuracy.cpp Activation.cpp Dataset.cpp Driver.cpp Export.cpp Fit.cpp Forward.cpp Interface.cpp Loss.cpp Optimize.cpp Parser.cpp Utilities.cpp /Qopenmp /Qunroll /Qipo /O3 /Ot /GT /Ob2 /Oi /GA /fp:precise /QxHost /Qstd:c++17 /FeNeural-Network.exe` +Then, to execute, use: +```powershell +nn.exe -i 784 -h 150 -h 100 -h 50 -o 10 +``` ## Model Settings @@ -30,17 +40,17 @@ The model's settings are: * Loss function: **MSE** * Learning rate: **0.1** -With these settings, the training is expected to last around *25 minutes* running on a medium to high-end machine. +With these settings, the training is expected to last around *17 minutes* running on a medium to high-end machine. ## Fine tuning -In `Common.hpp` there are parameters that can be tuned for better results. For example, there is a variable called `N_THREADS` that holds the number of threads to request from the OS. This number is recommended to be equal to the number of the system's *logical cores*. Furthermore, in this file the user can edit the number of *epochs* of training for faster results and the model's *learning rate*. +In `common.hpp` there are parameters that can be tuned for better results. ## Results -The effective core utilization percentage was around 97 %. An example of execution is: +An example of execution is: -![Expected Output](expected-output.PNG) +![Expected Output](docs/sample-run.PNG) Below, there are multiple model architectures compared for research purposes using the fashion MNIST dataset: @@ -57,7 +67,3 @@ Below, there are multiple model architectures compared for research purposes usi | 8 | 150 | 100 | 50 | Sigmoid | 1000 | 0.01 | 88.33 | 0.10240 | It's worth to mention that for the eighth model, the training accuracy was around 59200 out of 60000 and had a training loss equal to 0.00967. This means that for a feed-forward model, a test accuracy of 90 % on the test subset of the fashion MNIST dataset is a ceiling. - -## Notes - -There is a [project report](lab4-neural-network.pdf) in Greek. diff --git a/build/README.md b/build/README.md new file mode 100644 index 0000000..b261730 --- /dev/null +++ b/build/README.md @@ -0,0 +1,3 @@ +# Build + +This directory is used for building purposes by `make`. diff --git a/data/README.md b/data/README.md new file mode 100644 index 0000000..b008aa4 --- /dev/null +++ b/data/README.md @@ -0,0 +1,5 @@ +# Data + +In this directory, you have to download and store the 2 CSV files found on Kaggle as instructed by the main [README](../README.md) file. 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diff --git a/neural-network/Activation.hpp b/lib/activation.hpp similarity index 91% rename from neural-network/Activation.hpp rename to lib/activation.hpp index 47be18e..51f98fb 100644 --- a/neural-network/Activation.hpp +++ b/lib/activation.hpp @@ -1,25 +1,25 @@ -/** - * Activation.hpp - * - * In this header file, we define - * all neuron activation functions. - * Specifically, there is an - * implementation of the sigmoid - * and the ReLU activation function. - * There are also the corresponding - * derivatives of those functions - * to be used during neuron error - * computation (back propagation - * algorithm). - */ - -#pragma once - -#include "Common.hpp" - -double exp(double x); -double sigmoid(double x); -double fast_sigmoid(double x); -double sig_derivative(double x); -double relu(double x); -double rel_derivative(double x); +/** + * Activation.hpp + * + * In this header file, we define + * all neuron activation functions. + * Specifically, there is an + * implementation of the sigmoid + * and the ReLU activation function. + * There are also the corresponding + * derivatives of those functions + * to be used during neuron error + * computation (back propagation + * algorithm). + */ + +#pragma once + +#include "common.hpp" + +double exp(double x); +double sigmoid(double x); +double fast_sigmoid(double x); +double sig_derivative(double x); +double relu(double x); +double rel_derivative(double x); diff --git a/neural-network/Common.hpp b/lib/common.hpp similarity index 87% rename from neural-network/Common.hpp rename to lib/common.hpp index dbdc5d8..d8e5c17 100644 --- a/neural-network/Common.hpp +++ b/lib/common.hpp @@ -1,49 +1,49 @@ - -/** - * Common.hpp - * - * In this header file, we define the constants - * used throughout the project. We also - * include all the header files necessary to - * make the implementation work. - */ - -#pragma once - -#include /// ceil() -#include /// std::array -#include /// EINVAL -#include /// std::random -#include /// std::vector -#include /// std::numeric_limits -#include /// printf() -#include /// memmove() -#include /// std::setw -#include /// std::ostream -#include /// assert() -#include /// system() -#include /// std::cout -#include /// std::runtime_error -#include /// std::fill_n -#include /// intptr_t - -#include /// OpenMP Multiprocessing Programming Framework - -#define array_sizeof(type) ((char *)(&type+1)-(char*)(&type)) - /// Macro that computes the size of an array -typedef intptr_t ssize_t; /// Declares `ssize_t` type that is used in `Preprocessing.h` - -constexpr int EPOCHS = 100; /// Declares the number of epochs for the model's training -constexpr int N_THREADS = 12; /// Specifies the number of threads to request from the OS -constexpr int N_ACTIVATIONS = 2; /// Declares the number of neuron activation functions declared in the project -constexpr int CLI_WINDOW_WIDTH = 50; /// Defines the length of the progress bar for the project's CLI -constexpr int MNIST_CLASSES = 10; /// Declares the number of classes found in the MNIST dataset -constexpr double LEARNING_RATE = 0.1; /// Defines the learning rate for the neural network -constexpr double MNIST_TRAIN = 60000.0; /// Declares the number of training examples found in the MNIST dataset -constexpr double MNIST_TEST = 10000.0; /// Declares the number of evaluation examples found in the MNIST dataset -constexpr double EXP = 2.718282; /// Defines the exponential constant `e` -constexpr double MNIST_MAX_VAL = 255.0; /// Defines max value found in the input subset of theMNIST dataset -constexpr char TRAINING_DATA_FILEPATH[] = "../data/fashion-mnist_train.csv"; - /// Declares the filepath of the MNIST training CSV file -constexpr char EVALUATION_DATA_FILEPATH[] = "../data/fashion-mnist_test.csv"; - /// Declares the filepath of the MNIST evaluation CSV file + +/** + * common.hpp + * + * In this header file, we define the constants + * used throughout the project. We also + * include all the header files necessary to + * make the implementation work. + */ + +#pragma once + +#include "cmath" /// ceil() +#include /// std::array +#include /// EINVAL +#include /// std::random +#include /// std::vector +#include /// std::numeric_limits +#include /// printf() +#include /// memmove() +#include /// std::setw +#include /// std::ostream +#include /// assert() +#include /// system() +#include /// std::cout +#include /// std::runtime_error +#include /// std::fill_n +#include /// intptr_t + +#include /// OpenMP Multiprocessing Programming Framework + +#define array_sizeof(type) ((char *)(&type+1)-(char*)(&type)) + /// Macro that computes the size of an array +typedef intptr_t ssize_t; /// Declares `ssize_t` type that is used in `Preprocessing.h` + +constexpr int EPOCHS = 10; /// Declares the number of epochs for the model's training +constexpr int N_THREADS = 12; /// Specifies the number of threads to request from the OS +constexpr int N_ACTIVATIONS = 2; /// Declares the number of neuron activation functions declared in the project +constexpr int CLI_WINDOW_WIDTH = 50; /// Defines the length of the progress bar for the project's CLI +constexpr int MNIST_CLASSES = 10; /// Declares the number of classes found in the MNIST dataset +constexpr double LEARNING_RATE = 0.1; /// Defines the learning rate for the neural network +constexpr double MNIST_TRAIN = 60000.0; /// Declares the number of training examples found in the MNIST dataset +constexpr double MNIST_TEST = 10000.0; /// Declares the number of evaluation examples found in the MNIST dataset +constexpr double EXP = 2.718282; /// Defines the exponential constant `e` +constexpr double MNIST_MAX_VAL = 255.0; /// Defines max value found in the input subset of theMNIST dataset +constexpr char TRAINING_DATA_FILEPATH[] = "./data/fashion-mnist_train.csv"; + /// Declares the filepath of the MNIST training CSV file +constexpr char EVALUATION_DATA_FILEPATH[] = "./data/fashion-mnist_test.csv"; + /// Declares the filepath of the MNIST evaluation CSV file \ No newline at end of file diff --git a/neural-network/Dataset.hpp b/lib/dataset.hpp similarity index 93% rename from neural-network/Dataset.hpp rename to lib/dataset.hpp index df9f884..4487146 100644 --- a/neural-network/Dataset.hpp +++ b/lib/dataset.hpp @@ -1,61 +1,61 @@ -/** - * Preprocess.hpp - * - * In this header file, we define a - * class that handles a dataset. The class - * has a function that imports the dataset - * directly from a CSV file. There is also - * a function that prints out the input and - * expected output of a dataset instance. - * Finally, while parsing the CSV file, the - * data is split into input examples and - * the corresponding (expected) outputs. - */ - -#pragma once - -#include "Interface.hpp" - - /** - * Implementation of a dataset class. - * - * Upon a dataset creation, the developer - * has to call `read_csv` providing the - * requested arguments to start using - * the created dataset instance. The dataset - * has got an attribute (variable) `classes` - * which is initialized after the number of - * classes in a dataset. For the project's - * purposes, this attribute has been initialized - * with 10 (ten), since there are 10 classes in the - * MNIST fashion dataset. - */ -class dataset -{ -public: - int samples, dimensions, classes; - double** X, ** Y; - - ssize_t getline(char** lineptr, size_t* n, FILE* stream); - void read_csv(const char* filename, int dataset_flag, double x_max); - int get_label(int sample); - void print_dataset(void); - - dataset(int classes, int samples) : - classes{ classes }, - samples{ samples } - { - dimensions = 0; - } - - ~dataset() - { - for (int i = 0; i < samples; i += 1) - { - delete[] X[i]; - delete[] Y[i]; - } - delete[] X; - delete[] Y; - } -}; +/** + * dataset.hpp + * + * In this header file, we define a + * class that handles a dataset. The class + * has a function that imports the dataset + * directly from a CSV file. There is also + * a function that prints out the input and + * expected output of a dataset instance. + * Finally, while parsing the CSV file, the + * data is split into input examples and + * the corresponding (expected) outputs. + */ + +#pragma once + +#include "interface.hpp" + + /** + * Implementation of a dataset class. + * + * Upon a dataset creation, the developer + * has to call `read_csv` providing the + * requested arguments to start using + * the created dataset instance. The dataset + * has got an attribute (variable) `classes` + * which is initialized after the number of + * classes in a dataset. For the project's + * purposes, this attribute has been initialized + * with 10 (ten), since there are 10 classes in the + * MNIST fashion dataset. + */ +class dataset +{ +public: + int samples, dimensions, classes; + double** X, ** Y; + + ssize_t getline(char** lineptr, size_t* n, FILE* stream); + void read_csv(const char* filename, int dataset_flag, double x_max); + int get_label(int sample); + void print_dataset(void); + + dataset(int classes, int samples) : + classes{ classes }, + samples{ samples } + { + dimensions = 0; + } + + ~dataset() + { + for (int i = 0; i < samples; i += 1) + { + delete[] X[i]; + delete[] Y[i]; + } + delete[] X; + delete[] Y; + } +}; diff --git a/neural-network/Driver.hpp b/lib/driver.hpp similarity index 58% rename from neural-network/Driver.hpp rename to lib/driver.hpp index 8c68a6e..3ad8cc1 100644 --- a/neural-network/Driver.hpp +++ b/lib/driver.hpp @@ -1,13 +1,13 @@ -/** - * Driver.hpp - * - * In this header file, we include all the - * custom made header files used for the - * model's implementation. - */ - -#pragma once - -#include "Parser.hpp" -#include "Neural.hpp" -#include "Interface.hpp" +/** + * driver.hpp + * + * In this header file, we include all the + * custom made header files used for the + * model's implementation. + */ + +#pragma once + +#include "parser.hpp" +#include "neural.hpp" +#include "interface.hpp" diff --git a/neural-network/Interface.hpp b/lib/interface.hpp similarity index 94% rename from neural-network/Interface.hpp rename to lib/interface.hpp index 6bd75b9..ef49d88 100644 --- a/neural-network/Interface.hpp +++ b/lib/interface.hpp @@ -1,91 +1,91 @@ - -/** - * Interface.hpp - * - * In this header file, we implement a - * basic User Interface using the Command - * Line Window. The defined functions are - * used to print information about the data - * processed by the neural network, about - * the dataset, about the neural network's - * progress and help the user understand - * how to use the project. there is also a - * cross platform implementation of a progress - * bar. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ - -#pragma once - -#include "Common.hpp" - -#ifdef _WIN32 -#define _CRT_SECURE_NO_WARNINGS 1 -#include -#else -#include -#include -#include -#endif - -void setupConsole(void); -void restoreConsole(void); -void getWindowSize(int(&rows), int(&columns)); -void getCursorPosition(int* row, int* col); -void usage(char* filename); -void print_epoch_stats(int epoch, double epoch_loss, int epoch_accuracy, double benchmark); - -void moveUp(int positions); -void moveDown(int positions); -void scrollUp(int positions); -void scrollDown(int positions); -void clearScreen(void); -void gotoxy(int x, int y); -void hideCursor(void); -void showCursor(void); -void saveCursorPosition(void); -void restoreCursorPosition(void); - - -/** - * Implements a progress bar in CLI. - * - * Instances of this class are progress bars - * that inform the user of a large task's - * progress. There is also a short description - * attached to each progress bar. The description - * is recommended not to exceed the length of 35 - * characters. - */ -class progress_bar -{ -public: - std::string message; - char* bar; - char progress_token; - int progress; - int length; - - void indicate_progress(double checkpoint); - - progress_bar(std::string message, char progress_token, int length) : - message{ message }, - progress_token{ progress_token }, - length{ length } - { - bar = new char[length + 1]; - for (int i = 0; i < length; i += 1) - { - bar[i] = ' '; - } - bar[length] = '\0'; - progress = 0; - std::cout << "\n"; - } - - ~progress_bar() - { - delete[] bar; - } -}; + +/** + * interface.hpp + * + * In this header file, we implement a + * basic User Interface using the Command + * Line Window. The defined functions are + * used to print information about the data + * processed by the neural network, about + * the dataset, about the neural network's + * progress and help the user understand + * how to use the project. there is also a + * cross platform implementation of a progress + * bar. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ + +#pragma once + +#include "common.hpp" + +#ifdef _WIN32 +#define _CRT_SECURE_NO_WARNINGS 1 +#include +#else +#include +#include +#include +#endif + +void setupConsole(void); +void restoreConsole(void); +void getWindowSize(int(&rows), int(&columns)); +void getCursorPosition(int* row, int* col); +void usage(char* filename); +void print_epoch_stats(int epoch, double epoch_loss, int epoch_accuracy, double benchmark); + +void moveUp(int positions); +void moveDown(int positions); +void scrollUp(int positions); +void scrollDown(int positions); +void clearScreen(void); +void gotoxy(int x, int y); +void hideCursor(void); +void showCursor(void); +void saveCursorPosition(void); +void restoreCursorPosition(void); + + +/** + * Implements a progress bar in CLI. + * + * Instances of this class are progress bars + * that inform the user of a large task's + * progress. There is also a short description + * attached to each progress bar. The description + * is recommended not to exceed the length of 35 + * characters. + */ +class progress_bar +{ +public: + std::string message; + char* bar; + char progress_token; + int progress; + int length; + + void indicate_progress(double checkpoint); + + progress_bar(std::string message, char progress_token, int length) : + message{ message }, + progress_token{ progress_token }, + length{ length } + { + bar = new char[length + 1]; + for (int i = 0; i < length; i += 1) + { + bar[i] = ' '; + } + bar[length] = '\0'; + progress = 0; + std::cout << "\n"; + } + + ~progress_bar() + { + delete[] bar; + } +}; diff --git a/neural-network/Neural.hpp b/lib/neural.hpp similarity index 85% rename from neural-network/Neural.hpp rename to lib/neural.hpp index 5241390..7bfc08d 100644 --- a/neural-network/Neural.hpp +++ b/lib/neural.hpp @@ -1,85 +1,82 @@ -/** - * Neural.hpp - * - * In this header file, we define - * a template for the custom neural - * network. - */ - -#pragma once - -#include "Common.hpp" -#include "Dataset.hpp" -#include "Activation.hpp" - -/** - * Implements a Multi Layer Perceptron model. - * - * The neural network works with sigmoid - * activation function and Mean Squared - * Error loss function. However, there - * is potential for support of various - * activation and loss functions. The - * performance of the neural network - * class has been optimized with OpenMP - * framework. - */ -class nn -{ -public: - double** z, ** a, ** delta, *** weights; - - std::vector layers; - - void set_layers(const std::vector& l); - void set_z(const std::vector& l); - void set_a(const std::vector& l); - void set_delta(const std::vector& l); - void set_weights(const std::vector& l, const double min, const double max); - void compile(const std::vector& l, const double min, const double max); - void zero_grad(double* (&X)); - void forward(void); - void back_propagation(double* (&Y)); - void optimize(void); - int get_label(double* (&y_pred)); - int predict(double* (&X)); - double mse_loss(double* (&Y), int dim); - int accuracy(double* (&Y), int dim); - void fit(dataset(&TRAIN)); - void evaluate(dataset(&TEST)); - void export_weights(std::string filename); - void summary(void); - - nn() - { - - } - - ~nn() - { - for (int i = 0; i < layers.size(); i += 1) - { - delete[] z[i]; - delete[] a[i]; - } - delete[] z; - delete[] a; - for (int i = 0; i < layers.size() - 1; i += 1) - { - delete[] delta[i]; - } - delete[] delta; - for (int i = 1; i < layers.size(); i += 1) - { - for (int j = 0; j < layers[i] - 1; j += 1) - { - delete[] weights[i - 1][j]; - } - delete[] weights[i - 1]; - } - delete[] weights; - - layers.clear(); - layers.shrink_to_fit(); - } -}; +/** + * neural.hpp + * + * In this header file, we define + * a template for the custom neural + * network. + */ + +#pragma once + +#include "common.hpp" +#include "dataset.hpp" +#include "activation.hpp" + +/** + * Implements a Multi Layer Perceptron model. + * + * The neural network works with sigmoid + * activation function and Mean Squared + * Error loss function. However, there + * is potential for support of various + * activation and loss functions. + */ +class nn +{ +public: + double** z, ** a, ** delta, *** weights; + + std::vector layers; + + void set_layers(const std::vector& l); + void set_z(const std::vector& l); + void set_a(const std::vector& l); + void set_delta(const std::vector& l); + void set_weights(const std::vector& l, const double min, const double max); + void compile(const std::vector& l, const double min, const double max); + void zero_grad(double* (&X)); + void forward(void); + void back_propagation(double* (&Y)); + void optimize(void); + int get_label(double* (&y_pred)); + int predict(double* (&X)); + double mse_loss(double* (&Y), int dim); + int accuracy(double* (&Y), int dim); + void fit(dataset(&TRAIN)); + void evaluate(dataset(&TEST)); + void export_weights(std::string filename); + void summary(void); + + nn() + { + + } + + ~nn() + { + for (int i = 0; i < layers.size(); i += 1) + { + delete[] z[i]; + delete[] a[i]; + } + delete[] z; + delete[] a; + for (int i = 0; i < layers.size() - 1; i += 1) + { + delete[] delta[i]; + } + delete[] delta; + for (int i = 1; i < layers.size(); i += 1) + { + for (int j = 0; j < layers[i] - 1; j += 1) + { + delete[] weights[i - 1][j]; + } + delete[] weights[i - 1]; + } + delete[] weights; + + layers.clear(); + layers.shrink_to_fit(); + } +}; \ No newline at end of file diff --git a/neural-network/Parser.hpp b/lib/parser.hpp similarity index 86% rename from neural-network/Parser.hpp rename to lib/parser.hpp index a017f7a..420d362 100644 --- a/neural-network/Parser.hpp +++ b/lib/parser.hpp @@ -1,17 +1,17 @@ -/** - * Parser.hpp - * - * In this header file, we define all - * the functions that help parse the user's - * arguments. Those arguments include the - * sizes of the different layers to define - * a neural network. The user can also choose - * to review the programs usage. - */ - -#pragma once - -#include "Interface.hpp" - -int parse_integer(char* argv); -void parse_arguments(int argc, char* argv[], std::vector& vec); +/** + * parser.hpp + * + * In this header file, we define all + * the functions that help parse the user's + * arguments. Those arguments include the + * sizes of the different layers to define + * a neural network. The user can also choose + * to review the programs usage. + */ + +#pragma once + +#include "interface.hpp" + +int parse_integer(char* argv); +void parse_arguments(int argc, char* argv[], std::vector& vec); diff --git a/neural-network/Driver.cpp b/main.cpp similarity index 97% rename from neural-network/Driver.cpp rename to main.cpp index 9545810..61e23e8 100644 --- a/neural-network/Driver.cpp +++ b/main.cpp @@ -1,45 +1,45 @@ - -#include "Driver.hpp" - -/** - * Implements the driver for the Neural Network. - * - * @param[in] argc number of user arguments - * @param[in] argv vector of user arguments - * - * @return 0, if the executable was terminated normally - * - * @note For the driver to work properly, adjust the project settings found at the `Common.h` file. - * One such adjustment is to define the filepaths of the training and the evaluation subsets. - * Another strongly recommended change is the number of threads requested by the OS. This number - * is recommended to be equal to the number of the hosts's Logical Processors. This will very - * possibly optimize execution time and therefore increase performance. - */ -int main(int argc, char* argv[]) -{ - int cli_rows, cli_cols, cursor_row, cursor_col; - double start, end; - std::vector vec; - - nn fcn; /// Declares the image of the neural network - dataset TRAIN(MNIST_CLASSES, MNIST_TRAIN); /// Declares training data subset - dataset TEST(MNIST_CLASSES, MNIST_TEST); /// Declares evaluation data subset - - parse_arguments(argc, argv, vec); /// Parses user arguments - start = omp_get_wtime(); /// Initializes benchmark - - TRAIN.read_csv(TRAINING_DATA_FILEPATH, 0, MNIST_MAX_VAL); /// Initializes training data subset - TEST.read_csv(EVALUATION_DATA_FILEPATH, 1, MNIST_MAX_VAL); /// Initializes evaluation data subset - - fcn.compile(vec, -1.0, 1.0); /// Initializes the neural network's image - fcn.summary(); /// Prints model structure - fcn.fit(TRAIN); /// Trains the model - fcn.evaluate(TEST); /// Evaluates the model - fcn.export_weights("mnist-fcn"); - - end = omp_get_wtime(); /// Terminates the benchmark - - std::cout << "\n\nBenchmark results: " << end - start << " seconds\n"; /// Prints benchmark results - - return(0); -} + +#include "driver.hpp" + +/** + * Implements the driver for the Neural Network. + * + * @param[in] argc number of user arguments + * @param[in] argv vector of user arguments + * + * @return 0, if the executable was terminated normally + * + * @note For the driver to work properly, adjust the project settings found at the `Common.h` file. + * One such adjustment is to define the filepaths of the training and the evaluation subsets. + * Another strongly recommended change is the number of threads requested by the OS. This number + * is recommended to be equal to the number of the hosts's Logical Processors. This will very + * possibly optimize execution time and therefore increase performance. + */ +int main(int argc, char* argv[]) +{ + int cli_rows, cli_cols, cursor_row, cursor_col; + double start, end; + std::vector vec; + + nn fcn; /// Declares the image of the neural network + dataset TRAIN(MNIST_CLASSES, MNIST_TRAIN); /// Declares training data subset + dataset TEST(MNIST_CLASSES, MNIST_TEST); /// Declares evaluation data subset + + parse_arguments(argc, argv, vec); /// Parses user arguments + start = omp_get_wtime(); /// Initializes benchmark + + TRAIN.read_csv(TRAINING_DATA_FILEPATH, 0, MNIST_MAX_VAL); /// Initializes training data subset + TEST.read_csv(EVALUATION_DATA_FILEPATH, 1, MNIST_MAX_VAL); /// Initializes evaluation data subset + + fcn.compile(vec, -1.0, 1.0); /// Initializes the neural network's image + fcn.summary(); /// Prints model structure + fcn.fit(TRAIN); /// Trains the model + fcn.evaluate(TEST); /// Evaluates the model + fcn.export_weights("mnist-fcn"); + + end = omp_get_wtime(); /// Terminates the benchmark + + std::cout << "\n\nBenchmark results: " << end - start << " seconds\n"; /// Prints benchmark results + + return(0); +} diff --git a/makefile b/makefile index 83c3e38..3e4c28c 100644 --- a/makefile +++ b/makefile @@ -1,5 +1,5 @@ # Thanks to Job Vranish (https://spin.atomicobject.com/2016/08/26/makefile-c-projects/) -TARGET_EXEC := lab-4_mnist-fcn +TARGET_EXEC := nn.out CXX := g++ @@ -7,11 +7,13 @@ COMPILE_INF := -Wall -Wextra -fopt-info CXXFLAGS := -O3 -fopenmp -march=native -std=c++17 +DRIVER := main.cpp BUILD_DIR := ./build -SRC_DIRS := ./neural-network +SRC_DIRS := ./src +HEADER_DIRS := ./lib # Find all the C++ files we want to compile -SRCS := $(shell find $(SRC_DIRS) -name *.cpp) +SRCS := $(shell find $(SRC_DIRS)/*.cpp $(DRIVER)) # String substitution for every C++ file. # As an example, hello.cpp turns into ./build/hello.cpp.o @@ -22,7 +24,7 @@ OBJS := $(SRCS:%=$(BUILD_DIR)/%.o) DEPS := $(OBJS:.o=.d) # Every folder in ./src will need to be passed to G++ so that it can find header files -INC_DIRS := $(shell find $(SRC_DIRS) -type d) +INC_DIRS := $(shell find $(HEADER_DIRS) -type d) # Add a prefix to INC_DIRS. So moduleA would become -ImoduleA. G++ understands this -I flag INC_FLAGS := $(addprefix -I,$(INC_DIRS)) @@ -32,7 +34,7 @@ CPPFLAGS := $(INC_FLAGS) -MMD -MP # The final build step. # To turn on warnings and optimization information, add $(COMPILE_INF) -$(BUILD_DIR)/$(TARGET_EXEC): $(OBJS) +$(TARGET_EXEC): $(OBJS) $(CXX) $(CXXFLAGS) $(OBJS) -o $@ $(LDFLAGS) # Build step for C++ source @@ -48,9 +50,9 @@ clean: rm -r $(BUILD_DIR) run: - ./$(BUILD_DIR)/$(TARGET_EXEC) -i 784 -h 100 -o 10 + .$(TARGET_EXEC) -i 784 -h 100 -o 10 # Include the .d makefiles. The - at the front suppresses the errors of missing # Makefiles. Initially, all the .d files will be missing, and we don't want those # errors to show up. --include $(DEPS) +-include $(DEPS) \ No newline at end of file diff --git a/neural-network.sln b/neural-network.sln deleted file mode 100644 index c77e912..0000000 --- a/neural-network.sln +++ /dev/null @@ -1,31 +0,0 @@ - -Microsoft Visual Studio Solution File, Format Version 12.00 -# Visual Studio Version 16 -VisualStudioVersion = 16.0.30413.136 -MinimumVisualStudioVersion = 10.0.40219.1 -Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "neural-network", "neural-network\neural-network.vcxproj", "{755CB976-BE4A-4B17-906B-3720E7E9F83D}" -EndProject -Global - GlobalSection(SolutionConfigurationPlatforms) = preSolution - Debug|x64 = Debug|x64 - Debug|x86 = Debug|x86 - Release|x64 = Release|x64 - Release|x86 = Release|x86 - EndGlobalSection - GlobalSection(ProjectConfigurationPlatforms) = postSolution - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Debug|x64.ActiveCfg = Debug|x64 - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Debug|x64.Build.0 = Debug|x64 - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Debug|x86.ActiveCfg = Debug|Win32 - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Debug|x86.Build.0 = Debug|Win32 - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Release|x64.ActiveCfg = Release|x64 - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Release|x64.Build.0 = Release|x64 - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Release|x86.ActiveCfg = Release|Win32 - {755CB976-BE4A-4B17-906B-3720E7E9F83D}.Release|x86.Build.0 = Release|Win32 - EndGlobalSection - GlobalSection(SolutionProperties) = preSolution - HideSolutionNode = FALSE - EndGlobalSection - GlobalSection(ExtensibilityGlobals) = postSolution - SolutionGuid = {D29192AA-05FA-4C09-B1CF-C47D9BD6A1F3} - EndGlobalSection -EndGlobal diff --git a/neural-network/neural-network.vcxproj b/neural-network/neural-network.vcxproj deleted file mode 100644 index 967a190..0000000 --- a/neural-network/neural-network.vcxproj +++ /dev/null @@ -1,229 +0,0 @@ - - - - - Debug - Win32 - - - Release - Win32 - - - Debug - x64 - - - Release - x64 - - - - - - - - - - - - - - - - - - - - - - - - - - - 16.0 - Win32Proj - {755cb976-be4a-4b17-906b-3720e7e9f83d} - neuralnetwork - 10.0 - - - - Application - true - Intel C++ Compiler 19.1 - Unicode - - - Application - false - Intel C++ Compiler 19.1 - true - Unicode - - - Application - true - Intel C++ Compiler 19.1 - Unicode - - - Application - false - Intel C++ Compiler 19.1 - true - Unicode - - - - - - - - - - - - - - - - - - - - - true - - - false - - - true - - - false - - - - Level3 - true - false - stdcpp17 - MaxSpeedHighLevel - true - Speed - true - true - HOST - No - HOSTTarget - GenerateParallelCode - Cpp17Support - false - AnySuitable - Precise - Default - None - - - - Console - true - - - - - Level3 - false - true - true - false - stdcpp17 - Speed - true - true - HOST - No - HOSTTarget - GenerateParallelCode - Cpp17Support - NoIPO - MaxSpeedHighLevel - AnySuitable - Precise - Default - None - - - - Console - true - true - true - - - - - Level3 - true - false - stdcpp17 - MaxSpeedHighLevel - true - Speed - true - true - HOST - No - HOSTTarget - GenerateParallelCode - Cpp17Support - false - AnySuitable - Default - None - - Precise - - - Console - true - - - - - Level3 - false - true - true - false - stdcpp17 - Speed - true - true - HOST - No - HOSTTarget - GenerateParallelCode - Cpp17Support - NoIPO - MaxSpeedHighLevel - AnySuitable - Default - None - - Precise - - - Console - true - true - true - - - - - - \ No newline at end of file diff --git a/neural-network/neural-network.vcxproj.filters b/neural-network/neural-network.vcxproj.filters deleted file mode 100644 index 09266f4..0000000 --- a/neural-network/neural-network.vcxproj.filters +++ /dev/null @@ -1,78 +0,0 @@ - - - - - {4FC737F1-C7A5-4376-A066-2A32D752A2FF} - cpp;c;cc;cxx;c++;cppm;ixx;def;odl;idl;hpj;bat;asm;asmx - - - {93995380-89BD-4b04-88EB-625FBE52EBFB} - h;hh;hpp;hxx;h++;hm;inl;inc;ipp;xsd - - - {67DA6AB6-F800-4c08-8B7A-83BB121AAD01} - rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms - - - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - Source Files - - - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - Header Files - - - \ No newline at end of file diff --git a/neural-network/Accuracy.cpp b/src/accuracy.cpp similarity index 95% rename from neural-network/Accuracy.cpp rename to src/accuracy.cpp index df81839..68349c0 100644 --- a/neural-network/Accuracy.cpp +++ b/src/accuracy.cpp @@ -1,33 +1,33 @@ - -#include "Neural.hpp" - -/** - * Computes model accuracy. This function pools the element with the maximum value from the - * predictions vector and then uses the target vector to compute the accuracy of the given - * prediction. - * - * @param[in, out] Y the vector with the desired values - * @param[in] dim number of the vectors' elements - * - * @return 1 if the element with the maximum value from the predictions vector was accurate, else 0 - * - * @note the given vectors must be of the same length - * - * @note Although passed by reference, the `Y` placeholder is not altered. - */ -int nn::accuracy(double* (&Y), int dim) -{ - double max_val = -2.0; - int max_idx = 0; - - for (int i = 0; i < dim; i += 1) /// Iterate through the vector with the predictions - { - if (a[layers.size() - 1][i] > max_val) /// Find the neuron with the maximum (filtered) value - { - max_val = a[layers.size() - 1][i]; - max_idx = i; - } - } - - return Y[max_idx] > 0.9 ? 1 : 0; /// Computes the accuracy of the given prediction based on the elite neuron found after the previous iteration -} + +#include "neural.hpp" + +/** + * Computes model accuracy. This function pools the element with the maximum value from the + * predictions vector and then uses the target vector to compute the accuracy of the given + * prediction. + * + * @param[in, out] Y the vector with the desired values + * @param[in] dim number of the vectors' elements + * + * @return 1 if the element with the maximum value from the predictions vector was accurate, else 0 + * + * @note the given vectors must be of the same length + * + * @note Although passed by reference, the `Y` placeholder is not altered. + */ +int nn::accuracy(double* (&Y), int dim) +{ + double max_val = -2.0; + int max_idx = 0; + + for (int i = 0; i < dim; i += 1) /// Iterate through the vector with the predictions + { + if (a[layers.size() - 1][i] > max_val) /// Find the neuron with the maximum (filtered) value + { + max_val = a[layers.size() - 1][i]; + max_idx = i; + } + } + + return Y[max_idx] > 0.9 ? 1 : 0; /// Computes the accuracy of the given prediction based on the elite neuron found after the previous iteration +} \ No newline at end of file diff --git a/neural-network/Activation.cpp b/src/activation.cpp similarity index 95% rename from neural-network/Activation.cpp rename to src/activation.cpp index 68609e4..317e127 100644 --- a/neural-network/Activation.cpp +++ b/src/activation.cpp @@ -1,121 +1,121 @@ - -#include "Activation.hpp" - -/** - * Efficient implementation to calculate e raise to the power x. - * - * @param[in] x the numerical exponent of the power - * - * @return the double precision floating point result of the power operation. - * - * @note Returns approximate value of e^x using sum of first n terms of Taylor Series. - * - * @remark https://www.geeksforgeeks.org/program-to-efficiently-calculate-ex/ - */ -double exp(double x) -{ - double sum = 1.0; - int precision = 100; - - for (int i = precision - 1; i > 0; i -= 1) - { - sum = 1.0 + x * sum / (double)i; - } - - return sum; -} - -/** - * Filters the input using the sigmoid activation function. - * This implementation exploits Taylor series for fast computation. - * - * @param[in] x this is the double precision floating - * point variable to be filtered by the sigmoid - * - * @return the filtered value - * - * @note The implementation does not utilize the std::exp routine to - * avoid overflow failures. - */ -double fast_sigmoid(double x) -{ - if (x > 13.0) - { - return 1.0; - } - else if (x < -13.0) - { - return 0.0; - } - else - { - return 1.0 / (1.0 + exp(-x)); /// Sigmoid formula - } -} - -/** - * Safe implementation of sigmoid. This implementation gives a more precise result. - * - * @param[in] x this is the double precision floating - * point variable to be filtered by the sigmoid - * - * @return the filtered value - * - * @note The std::pow() function is low on performance. - */ -double sigmoid(double x) -{ - if (x > 45.0) - { - return 1.0; - } - else if (x < -45.0) - { - return 0.0; - } - else - { - return 1.0 / (1.0 + std::pow(EXP, -x)); - } -} - -/** - * Computes the derivative of the sigmoid function. - * This implementation uses already filtered values - * by the sigmoid to speed up the computation process. - * - * @param[in] x this is the double precision floating - * point variable to be differentiated - * - * @return the derivative of x with respect to the sigmoid function - */ -double sig_derivative(double x) -{ - return (x * (1.0 - x)); /// Sigmoid derivative formula -} - -/** - * Filters the input using the ReLU activation function. - * - * @param[in] x this is the double precision floating - * point variable to be filtered by the ReLU - * - * @return the filtered value - */ -double relu(double x) -{ - return (x > 0.0 ? x : 0.0); /// ReLU formula -} - -/** - * Computes the derivative of the ReLU function. - * - * @param[in] x this is the double precision floating - * point variable to be differentiated - * - * @return the derivative of x with respect to the ReLU function - */ -double rel_derivative(double x) -{ - return (x < 0.0 ? 0.0 : 1.0); /// ReLU derivative formula -} + +#include "activation.hpp" + +/** + * Efficient implementation to calculate e raise to the power x. + * + * @param[in] x the numerical exponent of the power + * + * @return the double precision floating point result of the power operation. + * + * @note Returns approximate value of e^x using sum of first n terms of Taylor Series. + * + * @remark https://www.geeksforgeeks.org/program-to-efficiently-calculate-ex/ + */ +double exp(double x) +{ + double sum = 1.0; + int precision = 100; + + for (int i = precision - 1; i > 0; i -= 1) + { + sum = 1.0 + x * sum / (double)i; + } + + return sum; +} + +/** + * Filters the input using the sigmoid activation function. + * This implementation exploits Taylor series for fast computation. + * + * @param[in] x this is the double precision floating + * point variable to be filtered by the sigmoid + * + * @return the filtered value + * + * @note The implementation does not utilize the std::exp routine to + * avoid overflow failures. + */ +double fast_sigmoid(double x) +{ + if (x > 13.0) + { + return 1.0; + } + else if (x < -13.0) + { + return 0.0; + } + else + { + return 1.0 / (1.0 + exp(-x)); /// Sigmoid formula + } +} + +/** + * Safe implementation of sigmoid. This implementation gives a more precise result. + * + * @param[in] x this is the double precision floating + * point variable to be filtered by the sigmoid + * + * @return the filtered value + * + * @note The std::pow() function is low on performance. + */ +double sigmoid(double x) +{ + if (x > 45.0) + { + return 1.0; + } + else if (x < -45.0) + { + return 0.0; + } + else + { + return 1.0 / (1.0 + std::pow(EXP, -x)); + } +} + +/** + * Computes the derivative of the sigmoid function. + * This implementation uses already filtered values + * by the sigmoid to speed up the computation process. + * + * @param[in] x this is the double precision floating + * point variable to be differentiated + * + * @return the derivative of x with respect to the sigmoid function + */ +double sig_derivative(double x) +{ + return (x * (1.0 - x)); /// Sigmoid derivative formula +} + +/** + * Filters the input using the ReLU activation function. + * + * @param[in] x this is the double precision floating + * point variable to be filtered by the ReLU + * + * @return the filtered value + */ +double relu(double x) +{ + return (x > 0.0 ? x : 0.0); /// ReLU formula +} + +/** + * Computes the derivative of the ReLU function. + * + * @param[in] x this is the double precision floating + * point variable to be differentiated + * + * @return the derivative of x with respect to the ReLU function + */ +double rel_derivative(double x) +{ + return (x < 0.0 ? 0.0 : 1.0); /// ReLU derivative formula +} diff --git a/neural-network/Dataset.cpp b/src/dataset.cpp similarity index 96% rename from neural-network/Dataset.cpp rename to src/dataset.cpp index 5d52d07..a03a656 100644 --- a/neural-network/Dataset.cpp +++ b/src/dataset.cpp @@ -1,242 +1,242 @@ - -#include "Dataset.hpp" - -/** - * Processes a line of a given file stream. - * - * @param[in] lineptr a vector of strings - * @param[in, out] n length of a given row - * @param[in] stream the file stream to parse - * - * @return integer type that is used to mask failure during read operation - * - * @remark The original code is public domain -- Will Hartung 4/9/09 - * Modifications, public domain as well, by Antti Haapala, 11/10/17 - * Switched to getc on 5/23/19 - * https://stackoverflow.com/questions/735126/are-there-alternate-implementations-of-gnu-getline-interface/ - */ -ssize_t dataset::getline(char** lineptr, size_t* n, FILE* stream) -{ - size_t read; - int character; - - if (lineptr == NULL || stream == NULL || n == NULL) - { - errno = EINVAL; - return -1; - } - - character = getc(stream); - - if (character == EOF) - { - return -1; - } - - if (*lineptr == NULL) - { - *lineptr = (char*)malloc(128); - - if (*lineptr == NULL) - { - return -1; - } - - *n = 128; - } - - read = 0; - - while (character != EOF) - { - if (read + 1 >= *n) - { - size_t new_size = *n + (*n >> 2); - - if (new_size < 128) - { - new_size = 128; - } - - char* new_ptr = (char*)realloc(*lineptr, new_size); - - if (new_ptr == NULL) - { - return -1; - } - *n = new_size; - *lineptr = new_ptr; - } - - ((unsigned char*)(*lineptr))[read++] = character; - - if (character == '\n') - { - break; - } - - character = getc(stream); - } - - (*lineptr)[read] = '\0'; - return read; -} - -/** - * Parses a CSV file and splits contents into input and output, to train a neural network. - * - * @param[in] filename the file path of the CSV file to parse - * @param[in] dataset_flag if `0`, then the function parses training data - * if `1`, then the function parses evaluation data - * @param[in] x_max this is used to normalize the dataset in range [0, 1] - * - * @note The structure of the csv must obey the following rules: - * * The first row contains the column descriptions - * * All other cells contain integers - * * The first column is the expected output - * * All other columns operate as input for the neural network - * - * @note The `dataset_flag` is nothing more than an indicator for the UI. In - * other words it only tells the function which message to display, the - * message about parsing either the training or the evaluation data subset. - */ - -void dataset::read_csv(const char* filename, int dataset_flag, double x_max) -{ - FILE* stream; - stream = fopen(filename, "r"); - - int read = 0, columns = 0, rows = 1, current_width, intval, y_idx; - char* line = NULL, delimiter[] = ","; - std::string training_message = "Loading training dataset"; - std::string evalation_message = "Loading evaluation dataset"; - size_t len = 0; - - - if (!stream) /// Mask failure in opening the given file - { - printf("The file %s was not opened\n", filename); - } - else - { - std::cout << "\n"; - - progress_bar progress{ dataset_flag == 0 ? training_message : evalation_message, '*', CLI_WINDOW_WIDTH }; - - rewind(stream); - - X = new double* [samples]; /// Initializes the X instance, which is the container with the neural network's input samples - Y = new double* [samples]; /// Initializes the Y instance, which is the container with the neural network's corresponding (expected) outputs - - if ((read = getline(&line, &len, stream)) != -1) /// Parse first row in the given CSV file - { - char* token = strtok(line, delimiter); /// Split row in tokens based on the file separator, which in this case is the comma character - - while (token != NULL) - { - token = strtok(NULL, delimiter); - columns += 1; /// Count number of columns - } - } - - dimensions = columns - 1; - - while ((read = getline(&line, &len, stream)) != -1) /// Do the same for all the other rows - { - - progress.indicate_progress((rows + 0.0) / samples); /// Use a progress bar as user interface since the dataset might be large - - char* token = strtok(line, delimiter); - - columns = 0; - - X[rows - 1] = new double[dimensions]; /// Initialize `X` placeholder. In this container, we will store the samples' input for the model - Y[rows - 1] = new double[classes]; /// Initialize `Y` placeholder. In this container, we will store the samples' corresponding (expected) to train the model - - if (sscanf(token, "%d", &intval) != 1) /// Masks invalid data error - { - fprintf(stderr, "error - not an integer"); /// Expecting integer value type data - } - - for (y_idx = 0; y_idx < classes; y_idx += 1) /// Convert integer to `Y` value for the model depending on the number of classes - { - if (y_idx == intval) - { - Y[rows - 1][y_idx] = 1.0; - } - else - { - Y[rows - 1][y_idx] = 0.0; - } - } - - while (token != NULL) /// Separate fetched line into input tokens using commas as separators - { - token = strtok(NULL, delimiter); - if (token != NULL) - { - if (sscanf(token, "%d", &intval) != 1) /// Mask string to number casting fault - { - fprintf(stderr, "error - not an integer"); - } - - X[rows - 1][columns] = (intval + 0.0) / x_max; /// Normalize data to avoid overflow. For the MNIST dataset, max cell value is 255 - columns += 1; - } - } - rows += 1; - } - fclose(stream); - - samples = rows - 1; - } -} - -/** - * Fetches the class label. The `Y` placeholder an element initialized - * with 1.0 (one) indicating the sample's class. All other elements are - * initialized with 0 (zero). - * - * @param[in] sample the index of the sample in the dataset - * - * @return an integer corresponding to the class of the given sample - * - * @note To solve double precision floating number comparison fault - * due to precision representation error, we use the greater - * operand, since 0.0 < 0.9 and 1.0 > 0.9. - */ -int dataset::get_label(int sample) -{ - int label; - - for (int i = 0; i < classes; i += 1) - { - if (Y[sample][i] > 0.9) - { - label = i; - } - } - - return label; -} - -/** - * Prints every sample in the dataset. - */ -void dataset::print_dataset(void) -{ - for (int i = 0; i < samples; i += 1) - { - std::cout << "Sample " << i << " :"; - for (int j = 0; j < dimensions; j += 1) - { - std::cout << X[i][j] << " "; - if (j % CLI_WINDOW_WIDTH == 0 && j != 0) /// If the number of elements in `X` placeholder is large, split the elements into multiple lines - { - std::cout << "\n"; - } - } - std::cout << "\tLabel: " << get_label(i); - std::cout << std::endl; - } -} + +#include "dataset.hpp" + +/** + * Processes a line of a given file stream. + * + * @param[in] lineptr a vector of strings + * @param[in, out] n length of a given row + * @param[in] stream the file stream to parse + * + * @return integer type that is used to mask failure during read operation + * + * @remark The original code is public domain -- Will Hartung 4/9/09 + * Modifications, public domain as well, by Antti Haapala, 11/10/17 + * Switched to getc on 5/23/19 + * https://stackoverflow.com/questions/735126/are-there-alternate-implementations-of-gnu-getline-interface/ + */ +ssize_t dataset::getline(char** lineptr, size_t* n, FILE* stream) +{ + size_t read; + int character; + + if (lineptr == NULL || stream == NULL || n == NULL) + { + errno = EINVAL; + return -1; + } + + character = getc(stream); + + if (character == EOF) + { + return -1; + } + + if (*lineptr == NULL) + { + *lineptr = (char*)malloc(128); + + if (*lineptr == NULL) + { + return -1; + } + + *n = 128; + } + + read = 0; + + while (character != EOF) + { + if (read + 1 >= *n) + { + size_t new_size = *n + (*n >> 2); + + if (new_size < 128) + { + new_size = 128; + } + + char* new_ptr = (char*)realloc(*lineptr, new_size); + + if (new_ptr == NULL) + { + return -1; + } + *n = new_size; + *lineptr = new_ptr; + } + + ((unsigned char*)(*lineptr))[read++] = character; + + if (character == '\n') + { + break; + } + + character = getc(stream); + } + + (*lineptr)[read] = '\0'; + return read; +} + +/** + * Parses a CSV file and splits contents into input and output, to train a neural network. + * + * @param[in] filename the file path of the CSV file to parse + * @param[in] dataset_flag if `0`, then the function parses training data + * if `1`, then the function parses evaluation data + * @param[in] x_max this is used to normalize the dataset in range [0, 1] + * + * @note The structure of the csv must obey the following rules: + * * The first row contains the column descriptions + * * All other cells contain integers + * * The first column is the expected output + * * All other columns operate as input for the neural network + * + * @note The `dataset_flag` is nothing more than an indicator for the UI. In + * other words it only tells the function which message to display, the + * message about parsing either the training or the evaluation data subset. + */ + +void dataset::read_csv(const char* filename, int dataset_flag, double x_max) +{ + FILE* stream; + stream = fopen(filename, "r"); + + int read = 0, columns = 0, rows = 1, current_width, intval, y_idx; + char* line = NULL, delimiter[] = ","; + std::string training_message = "Loading training dataset"; + std::string evalation_message = "Loading evaluation dataset"; + size_t len = 0; + + + if (!stream) /// Mask failure in opening the given file + { + printf("The file %s was not opened\n", filename); + } + else + { + std::cout << "\n"; + + progress_bar progress{ dataset_flag == 0 ? training_message : evalation_message, '*', CLI_WINDOW_WIDTH }; + + rewind(stream); + + X = new double* [samples]; /// Initializes the X instance, which is the container with the neural network's input samples + Y = new double* [samples]; /// Initializes the Y instance, which is the container with the neural network's corresponding (expected) outputs + + if ((read = getline(&line, &len, stream)) != -1) /// Parse first row in the given CSV file + { + char* token = strtok(line, delimiter); /// Split row in tokens based on the file separator, which in this case is the comma character + + while (token != NULL) + { + token = strtok(NULL, delimiter); + columns += 1; /// Count number of columns + } + } + + dimensions = columns - 1; + + while ((read = getline(&line, &len, stream)) != -1) /// Do the same for all the other rows + { + + progress.indicate_progress((rows + 0.0) / samples); /// Use a progress bar as user interface since the dataset might be large + + char* token = strtok(line, delimiter); + + columns = 0; + + X[rows - 1] = new double[dimensions]; /// Initialize `X` placeholder. In this container, we will store the samples' input for the model + Y[rows - 1] = new double[classes]; /// Initialize `Y` placeholder. In this container, we will store the samples' corresponding (expected) to train the model + + if (sscanf(token, "%d", &intval) != 1) /// Masks invalid data error + { + fprintf(stderr, "error - not an integer"); /// Expecting integer value type data + } + + for (y_idx = 0; y_idx < classes; y_idx += 1) /// Convert integer to `Y` value for the model depending on the number of classes + { + if (y_idx == intval) + { + Y[rows - 1][y_idx] = 1.0; + } + else + { + Y[rows - 1][y_idx] = 0.0; + } + } + + while (token != NULL) /// Separate fetched line into input tokens using commas as separators + { + token = strtok(NULL, delimiter); + if (token != NULL) + { + if (sscanf(token, "%d", &intval) != 1) /// Mask string to number casting fault + { + fprintf(stderr, "error - not an integer"); + } + + X[rows - 1][columns] = (intval + 0.0) / x_max; /// Normalize data to avoid overflow. For the MNIST dataset, max cell value is 255 + columns += 1; + } + } + rows += 1; + } + fclose(stream); + + samples = rows - 1; + } +} + +/** + * Fetches the class label. The `Y` placeholder an element initialized + * with 1.0 (one) indicating the sample's class. All other elements are + * initialized with 0 (zero). + * + * @param[in] sample the index of the sample in the dataset + * + * @return an integer corresponding to the class of the given sample + * + * @note To solve double precision floating number comparison fault + * due to precision representation error, we use the greater + * operand, since 0.0 < 0.9 and 1.0 > 0.9. + */ +int dataset::get_label(int sample) +{ + int label; + + for (int i = 0; i < classes; i += 1) + { + if (Y[sample][i] > 0.9) + { + label = i; + } + } + + return label; +} + +/** + * Prints every sample in the dataset. + */ +void dataset::print_dataset(void) +{ + for (int i = 0; i < samples; i += 1) + { + std::cout << "Sample " << i << " :"; + for (int j = 0; j < dimensions; j += 1) + { + std::cout << X[i][j] << " "; + if (j % CLI_WINDOW_WIDTH == 0 && j != 0) /// If the number of elements in `X` placeholder is large, split the elements into multiple lines + { + std::cout << "\n"; + } + } + std::cout << "\tLabel: " << get_label(i); + std::cout << std::endl; + } +} diff --git a/neural-network/Export.cpp b/src/export.cpp similarity index 96% rename from neural-network/Export.cpp rename to src/export.cpp index a3c584c..a85fef5 100644 --- a/neural-network/Export.cpp +++ b/src/export.cpp @@ -1,39 +1,39 @@ - -#include "Neural.hpp" - -/** - * Exports weights of neural network instance into a CSV file. - * - * @param[in] filename the CSV name - */ -void nn::export_weights(std::string filename) -{ - std::ofstream export_stream; /// Defines an output file stream - export_stream.open("../data/" + filename + ".csv"); /// Associates `export_stream` with a CSV file named after the `filename` variable - for (int i = 1; i < layers.size() - 1; i += 1) /// Loops through model's hidden layers - { - for (int j = 0; j < layers[i] - 1; j += 1) /// Loops through layer's synapses - { - export_stream << "Neuron " << j << " Layer " << i << ","; - for (int k = 0; k < layers[i - 1]; k += 1) - { - export_stream << weights[i - 1][j][k] << (j == layers[i] - 2 ? "" : ","); - } /// Export element of that array to the `export_stream` file stream - export_stream << std::endl; - } - export_stream << std::endl; - } - - for (int j = 0; j < layers[layers.size() - 1]; j += 1) /// Loops through model's output layers - { - export_stream << "Neuron " << j << " Layer " << layers.size() - 1 << ","; - for (int k = 0; k < layers[layers.size() - 1]; k += 1) - { - export_stream << weights[layers.size() - 2][j][k] << (j == layers[layers.size()] - 1 ? "" : ","); - } /// Export element of that array to the `export_stream` file stream - export_stream << std::endl; - } - export_stream << std::endl; - - export_stream.close(); /// Closes file stream -} + +#include "neural.hpp" + +/** + * Exports weights of neural network instance into a CSV file. + * + * @param[in] filename the CSV name + */ +void nn::export_weights(std::string filename) +{ + std::ofstream export_stream; /// Defines an output file stream + export_stream.open("../data/" + filename + ".csv"); /// Associates `export_stream` with a CSV file named after the `filename` variable + for (int i = 1; i < layers.size() - 1; i += 1) /// Loops through model's hidden layers + { + for (int j = 0; j < layers[i] - 1; j += 1) /// Loops through layer's synapses + { + export_stream << "Neuron " << j << " Layer " << i << ","; + for (int k = 0; k < layers[i - 1]; k += 1) + { + export_stream << weights[i - 1][j][k] << (j == layers[i] - 2 ? "" : ","); + } /// Export element of that array to the `export_stream` file stream + export_stream << std::endl; + } + export_stream << std::endl; + } + + for (int j = 0; j < layers[layers.size() - 1]; j += 1) /// Loops through model's output layers + { + export_stream << "Neuron " << j << " Layer " << layers.size() - 1 << ","; + for (int k = 0; k < layers[layers.size() - 1]; k += 1) + { + export_stream << weights[layers.size() - 2][j][k] << (j == layers[layers.size()] - 1 ? "" : ","); + } /// Export element of that array to the `export_stream` file stream + export_stream << std::endl; + } + export_stream << std::endl; + + export_stream.close(); /// Closes file stream +} \ No newline at end of file diff --git a/neural-network/Fit.cpp b/src/fit.cpp similarity index 97% rename from neural-network/Fit.cpp rename to src/fit.cpp index a9a4b50..17d7a92 100644 --- a/neural-network/Fit.cpp +++ b/src/fit.cpp @@ -1,72 +1,72 @@ - -#include "Neural.hpp" -#include "Interface.hpp" - -/** - * Trains the given model. The model is a simple multi- - * layer feed forward perceptron. - * - * @param[in, out] TRAIN the training dataset - * - * @note Although passed by reference, `TRAIN` is not altered. - */ -void nn::fit(dataset(&TRAIN)) -{ - int shuffled_idx; /// Decalres sample "pointer" - double start, end; /// Declares epoch benchmark checkpoints - std::array loss; /// Declares container for training loss - std::array validity; /// Declares container for training accuracy - - std::random_device rd; /// Initializes non-deterministic random generator - std::mt19937 gen(rd()); /// Seeds mersenne twister - std::uniform_int_distribution<> dist(0, TRAIN.samples - 1); /// Distribute results between 0 and sample count exclusive - /// Change this depending on the amount of loaded datasets - for (int epoch = 0; epoch < EPOCHS; epoch += 1) /// Trains model - { - loss[epoch] = 0.0; /// Initializes epoch's training loss - validity[epoch] = 0; /// Initializes epoch's training accuracy - - start = omp_get_wtime(); /// Benchmarks epoch - for (int sample = 0; sample < TRAIN.samples; sample += 1) /// Iterates through all examples of the training dataset - { - shuffled_idx = dist(gen); /// Selects a random example to avoid un-shuffled dataset event - zero_grad(TRAIN.X[shuffled_idx]); /// Resets the neurons of the neural network - forward(); /// Feeds forward the selected input - back_propagation(TRAIN.Y[shuffled_idx]); /// Computes the error for every neuron in the network - optimize(); /// Optimizes weights using pack propagation - loss[epoch] += mse_loss(TRAIN.Y[shuffled_idx], TRAIN.classes); /// Updates epoch's loss of the model - validity[epoch] += accuracy(TRAIN.Y[shuffled_idx], TRAIN.classes); /// Updates epoch's accuracy of the model - } - end = omp_get_wtime(); /// Terminates epoch's benchmark - - loss[epoch] /= (TRAIN.samples + 0.0); /// Averages epoch's loss of the model - print_epoch_stats(epoch + 1, loss[epoch], validity[epoch], end - start); /// Prints epoch's loss, accuracy and benchmark - } -} - -/** - * Evaluates the given model. - * - * @param[in, out] TEST the evaluation dataset - * - * @note Although passed by reference, `TEST` is not altered. - */ - -void nn::evaluate(dataset(&TEST)) -{ - int validity = 0; - double start, end, loss = 0.0; - - start = omp_get_wtime(); /// Benchmarks model's evaluation - for (int sample = 0; sample < TEST.samples; sample += 1) /// Iterates through all examples of the evaluation dataset - { - zero_grad(TEST.X[sample]); /// Resets the neurons of the neural network - forward(); /// Feeds forward the evaluation sample - loss += mse_loss(TEST.Y[sample], TEST.classes); /// Updates loss of the model based on the evaluation set - validity += accuracy(TEST.Y[sample], TEST.classes); /// Updates accuracy of the model based on the evaluation set - } - end = omp_get_wtime(); /// Terminates model's evaluation benchmark - - loss /= (TEST.samples + 0.0); - print_epoch_stats(-1, loss, validity, end - start); /// Prints evaluation loss, accuracy, and benchmark -} + +#include "neural.hpp" +#include "interface.hpp" + +/** + * Trains the given model. The model is a simple multi- + * layer feed forward perceptron. + * + * @param[in, out] TRAIN the training dataset + * + * @note Although passed by reference, `TRAIN` is not altered. + */ +void nn::fit(dataset(&TRAIN)) +{ + int shuffled_idx; /// Decalres sample "pointer" + double start, end; /// Declares epoch benchmark checkpoints + std::array loss; /// Declares container for training loss + std::array validity; /// Declares container for training accuracy + + std::random_device rd; /// Initializes non-deterministic random generator + std::mt19937 gen(rd()); /// Seeds mersenne twister + std::uniform_int_distribution<> dist(0, TRAIN.samples - 1); /// Distribute results between 0 and sample count exclusive + /// Change this depending on the amount of loaded datasets + for (int epoch = 0; epoch < EPOCHS; epoch += 1) /// Trains model + { + loss[epoch] = 0.0; /// Initializes epoch's training loss + validity[epoch] = 0; /// Initializes epoch's training accuracy + + start = omp_get_wtime(); /// Benchmarks epoch + for (int sample = 0; sample < TRAIN.samples; sample += 1) /// Iterates through all examples of the training dataset + { + shuffled_idx = dist(gen); /// Selects a random example to avoid un-shuffled dataset event + zero_grad(TRAIN.X[shuffled_idx]); /// Resets the neurons of the neural network + forward(); /// Feeds forward the selected input + back_propagation(TRAIN.Y[shuffled_idx]); /// Computes the error for every neuron in the network + optimize(); /// Optimizes weights using pack propagation + loss[epoch] += mse_loss(TRAIN.Y[shuffled_idx], TRAIN.classes); /// Updates epoch's loss of the model + validity[epoch] += accuracy(TRAIN.Y[shuffled_idx], TRAIN.classes); /// Updates epoch's accuracy of the model + } + end = omp_get_wtime(); /// Terminates epoch's benchmark + + loss[epoch] /= (TRAIN.samples + 0.0); /// Averages epoch's loss of the model + print_epoch_stats(epoch + 1, loss[epoch], validity[epoch], end - start); /// Prints epoch's loss, accuracy and benchmark + } +} + +/** + * Evaluates the given model. + * + * @param[in, out] TEST the evaluation dataset + * + * @note Although passed by reference, `TEST` is not altered. + */ + +void nn::evaluate(dataset(&TEST)) +{ + int validity = 0; + double start, end, loss = 0.0; + + start = omp_get_wtime(); /// Benchmarks model's evaluation + for (int sample = 0; sample < TEST.samples; sample += 1) /// Iterates through all examples of the evaluation dataset + { + zero_grad(TEST.X[sample]); /// Resets the neurons of the neural network + forward(); /// Feeds forward the evaluation sample + loss += mse_loss(TEST.Y[sample], TEST.classes); /// Updates loss of the model based on the evaluation set + validity += accuracy(TEST.Y[sample], TEST.classes); /// Updates accuracy of the model based on the evaluation set + } + end = omp_get_wtime(); /// Terminates model's evaluation benchmark + + loss /= (TEST.samples + 0.0); + print_epoch_stats(-1, loss, validity, end - start); /// Prints evaluation loss, accuracy, and benchmark +} diff --git a/neural-network/Forward.cpp b/src/forward.cpp similarity index 97% rename from neural-network/Forward.cpp rename to src/forward.cpp index a3ee5f3..6a7b215 100644 --- a/neural-network/Forward.cpp +++ b/src/forward.cpp @@ -1,53 +1,53 @@ - -#include "Neural.hpp" - -/** - * Feeds forward the given model a given input vector. - * - * @note To exploit the full capabilities of the OpenMP framework, we use `collapse()` routine wherever possible. - * To use this routine, the given vector must be contiguous, therefore a 2D dynamic array cannot be collapsed. - * That's why there are temporary variables called `REGISTERS`, which are the 1D temporary image of those 2D - * vectors. Those registers are used during the parallel computations, and then we utilize the `memmove()` - * routine which has O(1) time complexity and transfers the computations back to the 2D vectors. - */ -void nn::forward(void) -{ - for (int layer = 1; layer < layers.size() - 1; layer += 1) - { -#pragma omp parallel for num_threads(N_THREADS) - for (int neuron = 0; neuron < layers[layer] - 1; neuron += 1) /// Iterates through the hidden layer's neurons - { - double REGISTER = 0.0; -#pragma omp simd reduction(+ : REGISTER) - for (int synapse = 0; synapse < layers[layer - 1]; synapse += 1) /// Iterates throught the previous layer - { - REGISTER += weights[layer - 1][neuron][synapse] * a[layer - 1][synapse]; /// Implements forward propagation for all hidden layers - } - z[layer][neuron] = REGISTER; - } - -#pragma omp parallel for num_threads(N_THREADS) - for (int neuron = 0; neuron < layers[layer] - 1; neuron += 1) - { - a[layer][neuron] = sigmoid(z[layer][neuron]); /// Applies model's activation function to computed results - } - } - -#pragma omp parallel for num_threads(N_THREADS) - for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) - { - double REGISTER = 0.0; -#pragma omp simd reduction(+ : REGISTER) - for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) - { - REGISTER += weights[layers.size() - 2][neuron][synapse] * a[layers.size() - 2][synapse]; /// Implements forward propagation for the output layer - } - z[layers.size() - 1][neuron] = REGISTER; - } - -#pragma omp parallel for num_threads(N_THREADS) - for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) - { - a[layers.size() - 1][neuron] = sigmoid(z[layers.size() - 1][neuron]); /// Applies model's activation function to computed results - } /// Deallocates the temporary container off the memory -} + +#include "neural.hpp" + +/** + * Feeds forward the given model a given input vector. + * + * @note To exploit the full capabilities of the OpenMP framework, we use `collapse()` routine wherever possible. + * To use this routine, the given vector must be contiguous, therefore a 2D dynamic array cannot be collapsed. + * That's why there are temporary variables called `REGISTERS`, which are the 1D temporary image of those 2D + * vectors. Those registers are used during the parallel computations, and then we utilize the `memmove()` + * routine which has O(1) time complexity and transfers the computations back to the 2D vectors. + */ +void nn::forward(void) +{ + for (int layer = 1; layer < layers.size() - 1; layer += 1) + { +#pragma omp parallel for num_threads(N_THREADS) + for (int neuron = 0; neuron < layers[layer] - 1; neuron += 1) /// Iterates through the hidden layer's neurons + { + double REGISTER = 0.0; +#pragma omp simd reduction(+ : REGISTER) + for (int synapse = 0; synapse < layers[layer - 1]; synapse += 1) /// Iterates throught the previous layer + { + REGISTER += weights[layer - 1][neuron][synapse] * a[layer - 1][synapse]; /// Implements forward propagation for all hidden layers + } + z[layer][neuron] = REGISTER; + } + +#pragma omp parallel for num_threads(N_THREADS) + for (int neuron = 0; neuron < layers[layer] - 1; neuron += 1) + { + a[layer][neuron] = sigmoid(z[layer][neuron]); /// Applies model's activation function to computed results + } + } + +#pragma omp parallel for num_threads(N_THREADS) + for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) + { + double REGISTER = 0.0; +#pragma omp simd reduction(+ : REGISTER) + for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) + { + REGISTER += weights[layers.size() - 2][neuron][synapse] * a[layers.size() - 2][synapse]; /// Implements forward propagation for the output layer + } + z[layers.size() - 1][neuron] = REGISTER; + } + +#pragma omp parallel for num_threads(N_THREADS) + for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) + { + a[layers.size() - 1][neuron] = sigmoid(z[layers.size() - 1][neuron]); /// Applies model's activation function to computed results + } /// Deallocates the temporary container off the memory +} diff --git a/neural-network/Interface.cpp b/src/interface.cpp similarity index 96% rename from neural-network/Interface.cpp rename to src/interface.cpp index 4322e8a..bca332d 100644 --- a/neural-network/Interface.cpp +++ b/src/interface.cpp @@ -1,339 +1,339 @@ - -#include "Interface.hpp" - -/** - * Includes graphic libraries depending on the host's OS. - * Sets up CLI environment and defines functions and modules - * for the CLI. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -#ifdef _WIN32 - -#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING -#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004 -#endif - -static HANDLE stdoutHandle, stdinHandle; -static DWORD outModeInit, inModeInit; - -/** - * Sets up console (CLI) in Windows OS. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void setupConsole(void) -{ - DWORD outMode = 0, inMode = 0; - stdoutHandle = GetStdHandle(STD_OUTPUT_HANDLE); - stdinHandle = GetStdHandle(STD_INPUT_HANDLE); - - if (stdoutHandle == INVALID_HANDLE_VALUE || stdinHandle == INVALID_HANDLE_VALUE) - { - exit(GetLastError()); - } - - if (!GetConsoleMode(stdoutHandle, &outMode) || !GetConsoleMode(stdinHandle, &inMode)) - { - exit(GetLastError()); - } - - outModeInit = outMode; - inModeInit = inMode; - outMode |= ENABLE_VIRTUAL_TERMINAL_PROCESSING; /// Enable ANSI escape codes - inMode &= ~(ENABLE_ECHO_INPUT | ENABLE_LINE_INPUT); /// Set stdin as no echo and unbuffered - - if (!SetConsoleMode(stdoutHandle, outMode) || !SetConsoleMode(stdinHandle, inMode)) - { - exit(GetLastError()); - } -} - -/** - * Resets console (CLI) in Windows OS. During - * the execution there might be some changes - * in the CLI which need to be unmade upon - * program's termination. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void restoreConsole(void) -{ - printf("\x1b[0m"); /// Reset colors - - if (!SetConsoleMode(stdoutHandle, outModeInit) || !SetConsoleMode(stdinHandle, inModeInit)) - { /// Reset console mode - exit(GetLastError()); - } -} - -/** - * Evaluates CLI window size in Windows OS. - * - * @param[in, out] rows the number of rows of interface found - * @param[in, out] columns the number of columns of interface found - * - * @remark https://stackoverflow.com/questions/23369503/get-size-of-terminal-window-rows-columns - */ -void getWindowSize(int (&rows), int (&columns)) -{ - CONSOLE_SCREEN_BUFFER_INFO csbi; - - GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi); - columns = csbi.srWindow.Right - csbi.srWindow.Left + 1; - rows = csbi.srWindow.Bottom - csbi.srWindow.Top + 1; -} -#else - -static struct termios orig_term; -static struct termios new_term; - -/** - * Sets up console (CLI) in Unix OS. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void setupConsole(void) -{ - tcgetattr(STDIN_FILENO, &orig_term); - new_term = orig_term; - - new_term.c_lflag &= ~(ICANON | ECHO); - - tcsetattr(STDIN_FILENO, TCSANOW, &new_term); -} - -/** - * Resets console (CLI) in Unix OS. During - * the execution there might be some changes - * in the CLI which need to be unmade upon - * program's termination. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void restoreConsole(void) -{ - printf("\x1b[0m"); /// Reset colors - tcsetattr(STDIN_FILENO, TCSANOW, &orig_term); /// Reset console mode -} - -/** - * Evaluates CLI window size in Unix OS. - * - * @param[in, out] rows the number of rows of interface found - * @param[in, out] columns the number of columns of interface found - * - * @remark https://stackoverflow.com/questions/23369503/get-size-of-terminal-window-rows-columns - */ -void getWindowSize(int(&rows), int(&columns)) -{ - struct winsize w; - ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); - rows = w.ws_row; - columns = w.ws_col; -} -#endif - -/** - * Fetches cursor position with respect to the CLI. - * - * @param[in, out] row the row of the cursor in the CLI - * @param[in, out] col the column of the cursor in the CLI - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void getCursorPosition(int* row, int* col) -{ - printf("\x1b[6n"); - char buff[128]; - int indx = 0; - for (;;) - { - int cc = getchar(); - buff[indx] = (char)cc; - indx++; - if (cc == 'R') - { - buff[indx + 1] = '\0'; - break; - } - } - sscanf(buff, "\x1b[%d;%dR", row, col); - fseek(stdin, 0, SEEK_END); -} - -/** - * Moves CLI cursor Up. - * - * @param[in] positions number of rows to move cursor up - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void moveUp(int positions) -{ - printf("\x1b[%dA", positions); -} - -/** - * Moves CLI cursor Down. - * - * @param[in] positions number of rows to move cursor down - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void moveDown(int positions) -{ - printf("\x1b[%dB", positions); -} - -/** - * Scrolls CLI window Up. - * - * @param[in] positions number of rows to scroll up - * - * @remark https://en.wikipedia.org/wiki/ANSI_escape_code - */ -void scrollUp(int positions) -{ - printf("\x1b[%dS", positions); -} - -/** - * Scrolls CLI window Down. - * - * @param[in] positions number of rows to scroll down - * - * @remark https://en.wikipedia.org/wiki/ANSI_escape_code - */ -void scrollDown(int positions) -{ - printf("\x1b[%dT", positions); -} - -/** - * Clears CLI screen. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void clearScreen(void) -{ - printf("\x1b[2J\x1b[H"); -} - -/** - * Places CLI cursor at defined position in the CLI. - * - * @param[in] x the row to place the cursor - * @param[in] y the column to place the cursor - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - * - * @remark https://stackoverflow.com/questions/10401724/move-text-cursor-to-particular-screen-coordinate - */ -void gotoxy(int x, int y) -{ - printf("\x1b[%d;%df", x, y); -} - -/** - * Hides the cursor from the CLI. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void hideCursor(void) -{ - printf("\x1b[?25l"); -} - -/** - * Makes CLI cursor visible. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void showCursor(void) -{ - printf("\x1b[?25h"); -} - -/** - * Saves cursor's position in the CLI. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void saveCursorPosition(void) -{ - printf("\x1b%d", 7); -} - -/** - * Restores cursor's position in the CLI. - * - * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples - */ -void restoreCursorPosition(void) -{ - printf("\x1b%d", 8); -} - -/** - * Indicates progress of an operation. - * - * @param[in] percentage completion rate of the monitored task - */ -void progress_bar::indicate_progress(double checkpoint) -{ - std::cout << "\r" << message << "\t|"; - int ratio = (int)std::ceil(checkpoint * length); - int completion_percentage = (int)std::ceil(checkpoint * 100); - if (ratio > length) - { - ratio -= 1; - } - if (completion_percentage > 100) - { - completion_percentage -= 1; - } - for (int i = 0; i < ratio; i += 1) - { - bar[i] = progress_token; - } - std::cout << bar << "| " << std::setw(4) << completion_percentage << "%"; -} - - -/** - * Prints epoch stats. More specifically, it prints the epoch's number - * along with the model's accuracy and loss. It also prints the epoch's benchmark. - * - * @param[in] epoch the epoch's number - * @param[in] epoch_loss the model's loss during a certain epoch of training or evaluation - * @param[in] epoch_accuracy the model's accuracy during a certain epoch of training or evaluation - * @param[in] benchmark the epoch's benchmark - */ -void print_epoch_stats(int epoch, double epoch_loss, int epoch_accuracy, double benchmark) -{ - if (epoch == -1) - { - std::cout << "\n\n[EVALUATION] [LOSS " << std::fixed << std::setprecision(5) << epoch_loss << "] [ACCURACY " << std::setw(6) << epoch_accuracy << " out of " << (int)MNIST_TEST << "] Work took " << std::setw(4) << (int)benchmark << " seconds"; - } - else - { - std::cout << "\n[EPOCH " << std::setw(4) << epoch << "] [LOSS " << std::fixed << std::setprecision(5) << epoch_loss << "] [ACCURACY " << std::setw(6) << epoch_accuracy << " out of " << (int)MNIST_TRAIN << "] Work took " << std::setw(4) << (int)benchmark << " seconds"; - } -} - -/** - * Prints information regarding the usage and the available options of the project. - * - * @param[in] filename the filepath of the executable - * - * @note Upon this function's execution, the program is terminated. - */ -void usage(char* filename) -{ - std::cout << "Usage of " << filename << ":\n"; - std::cout << "\t:option \'-i\': integer \t - \t The size of the input layer for the neural network.\n"; - std::cout << "\t:option \'-h\': integer \t - \t The size of a hidden layer for the neural network.\n\t\t\t\t\t There can be multiple hidden layers. For every hidden layer, use this option.\n"; - std::cout << "\t:option \'-o\': integer \t - \t The size of the output layer for the neural network.\n"; - exit(8); -} + +#include "interface.hpp" + +/** + * Includes graphic libraries depending on the host's OS. + * Sets up CLI environment and defines functions and modules + * for the CLI. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +#ifdef _WIN32 + +#ifndef ENABLE_VIRTUAL_TERMINAL_PROCESSING +#define ENABLE_VIRTUAL_TERMINAL_PROCESSING 0x0004 +#endif + +static HANDLE stdoutHandle, stdinHandle; +static DWORD outModeInit, inModeInit; + +/** + * Sets up console (CLI) in Windows OS. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void setupConsole(void) +{ + DWORD outMode = 0, inMode = 0; + stdoutHandle = GetStdHandle(STD_OUTPUT_HANDLE); + stdinHandle = GetStdHandle(STD_INPUT_HANDLE); + + if (stdoutHandle == INVALID_HANDLE_VALUE || stdinHandle == INVALID_HANDLE_VALUE) + { + exit(GetLastError()); + } + + if (!GetConsoleMode(stdoutHandle, &outMode) || !GetConsoleMode(stdinHandle, &inMode)) + { + exit(GetLastError()); + } + + outModeInit = outMode; + inModeInit = inMode; + outMode |= ENABLE_VIRTUAL_TERMINAL_PROCESSING; /// Enable ANSI escape codes + inMode &= ~(ENABLE_ECHO_INPUT | ENABLE_LINE_INPUT); /// Set stdin as no echo and unbuffered + + if (!SetConsoleMode(stdoutHandle, outMode) || !SetConsoleMode(stdinHandle, inMode)) + { + exit(GetLastError()); + } +} + +/** + * Resets console (CLI) in Windows OS. During + * the execution there might be some changes + * in the CLI which need to be unmade upon + * program's termination. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void restoreConsole(void) +{ + printf("\x1b[0m"); /// Reset colors + + if (!SetConsoleMode(stdoutHandle, outModeInit) || !SetConsoleMode(stdinHandle, inModeInit)) + { /// Reset console mode + exit(GetLastError()); + } +} + +/** + * Evaluates CLI window size in Windows OS. + * + * @param[in, out] rows the number of rows of interface found + * @param[in, out] columns the number of columns of interface found + * + * @remark https://stackoverflow.com/questions/23369503/get-size-of-terminal-window-rows-columns + */ +void getWindowSize(int (&rows), int (&columns)) +{ + CONSOLE_SCREEN_BUFFER_INFO csbi; + + GetConsoleScreenBufferInfo(GetStdHandle(STD_OUTPUT_HANDLE), &csbi); + columns = csbi.srWindow.Right - csbi.srWindow.Left + 1; + rows = csbi.srWindow.Bottom - csbi.srWindow.Top + 1; +} +#else + +static struct termios orig_term; +static struct termios new_term; + +/** + * Sets up console (CLI) in Unix OS. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void setupConsole(void) +{ + tcgetattr(STDIN_FILENO, &orig_term); + new_term = orig_term; + + new_term.c_lflag &= ~(ICANON | ECHO); + + tcsetattr(STDIN_FILENO, TCSANOW, &new_term); +} + +/** + * Resets console (CLI) in Unix OS. During + * the execution there might be some changes + * in the CLI which need to be unmade upon + * program's termination. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void restoreConsole(void) +{ + printf("\x1b[0m"); /// Reset colors + tcsetattr(STDIN_FILENO, TCSANOW, &orig_term); /// Reset console mode +} + +/** + * Evaluates CLI window size in Unix OS. + * + * @param[in, out] rows the number of rows of interface found + * @param[in, out] columns the number of columns of interface found + * + * @remark https://stackoverflow.com/questions/23369503/get-size-of-terminal-window-rows-columns + */ +void getWindowSize(int(&rows), int(&columns)) +{ + struct winsize w; + ioctl(STDOUT_FILENO, TIOCGWINSZ, &w); + rows = w.ws_row; + columns = w.ws_col; +} +#endif + +/** + * Fetches cursor position with respect to the CLI. + * + * @param[in, out] row the row of the cursor in the CLI + * @param[in, out] col the column of the cursor in the CLI + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void getCursorPosition(int* row, int* col) +{ + printf("\x1b[6n"); + char buff[128]; + int indx = 0; + for (;;) + { + int cc = getchar(); + buff[indx] = (char)cc; + indx++; + if (cc == 'R') + { + buff[indx + 1] = '\0'; + break; + } + } + sscanf(buff, "\x1b[%d;%dR", row, col); + fseek(stdin, 0, SEEK_END); +} + +/** + * Moves CLI cursor Up. + * + * @param[in] positions number of rows to move cursor up + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void moveUp(int positions) +{ + printf("\x1b[%dA", positions); +} + +/** + * Moves CLI cursor Down. + * + * @param[in] positions number of rows to move cursor down + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void moveDown(int positions) +{ + printf("\x1b[%dB", positions); +} + +/** + * Scrolls CLI window Up. + * + * @param[in] positions number of rows to scroll up + * + * @remark https://en.wikipedia.org/wiki/ANSI_escape_code + */ +void scrollUp(int positions) +{ + printf("\x1b[%dS", positions); +} + +/** + * Scrolls CLI window Down. + * + * @param[in] positions number of rows to scroll down + * + * @remark https://en.wikipedia.org/wiki/ANSI_escape_code + */ +void scrollDown(int positions) +{ + printf("\x1b[%dT", positions); +} + +/** + * Clears CLI screen. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void clearScreen(void) +{ + printf("\x1b[2J\x1b[H"); +} + +/** + * Places CLI cursor at defined position in the CLI. + * + * @param[in] x the row to place the cursor + * @param[in] y the column to place the cursor + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + * + * @remark https://stackoverflow.com/questions/10401724/move-text-cursor-to-particular-screen-coordinate + */ +void gotoxy(int x, int y) +{ + printf("\x1b[%d;%df", x, y); +} + +/** + * Hides the cursor from the CLI. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void hideCursor(void) +{ + printf("\x1b[?25l"); +} + +/** + * Makes CLI cursor visible. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void showCursor(void) +{ + printf("\x1b[?25h"); +} + +/** + * Saves cursor's position in the CLI. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void saveCursorPosition(void) +{ + printf("\x1b%d", 7); +} + +/** + * Restores cursor's position in the CLI. + * + * @remark https://github.com/sol-prog/ansi-escape-codes-windows-posix-terminals-c-programming-examples + */ +void restoreCursorPosition(void) +{ + printf("\x1b%d", 8); +} + +/** + * Indicates progress of an operation. + * + * @param[in] percentage completion rate of the monitored task + */ +void progress_bar::indicate_progress(double checkpoint) +{ + std::cout << "\r" << message << "\t|"; + int ratio = (int)std::ceil(checkpoint * length); + int completion_percentage = (int)std::ceil(checkpoint * 100); + if (ratio > length) + { + ratio -= 1; + } + if (completion_percentage > 100) + { + completion_percentage -= 1; + } + for (int i = 0; i < ratio; i += 1) + { + bar[i] = progress_token; + } + std::cout << bar << "| " << std::setw(4) << completion_percentage << "%"; +} + + +/** + * Prints epoch stats. More specifically, it prints the epoch's number + * along with the model's accuracy and loss. It also prints the epoch's benchmark. + * + * @param[in] epoch the epoch's number + * @param[in] epoch_loss the model's loss during a certain epoch of training or evaluation + * @param[in] epoch_accuracy the model's accuracy during a certain epoch of training or evaluation + * @param[in] benchmark the epoch's benchmark + */ +void print_epoch_stats(int epoch, double epoch_loss, int epoch_accuracy, double benchmark) +{ + if (epoch == -1) + { + std::cout << "\n\n[EVALUATION] [LOSS " << std::fixed << std::setprecision(5) << epoch_loss << "] [ACCURACY " << std::setw(6) << epoch_accuracy << " out of " << (int)MNIST_TEST << "] Work took " << std::setw(4) << (int)benchmark << " seconds"; + } + else + { + std::cout << "\n[EPOCH " << std::setw(4) << epoch << "] [LOSS " << std::fixed << std::setprecision(5) << epoch_loss << "] [ACCURACY " << std::setw(6) << epoch_accuracy << " out of " << (int)MNIST_TRAIN << "] Work took " << std::setw(4) << (int)benchmark << " seconds"; + } +} + +/** + * Prints information regarding the usage and the available options of the project. + * + * @param[in] filename the filepath of the executable + * + * @note Upon this function's execution, the program is terminated. + */ +void usage(char* filename) +{ + std::cout << "Usage of " << filename << ":\n"; + std::cout << "\t:option \'-i\': integer \t - \t The size of the input layer for the neural network.\n"; + std::cout << "\t:option \'-h\': integer \t - \t The size of a hidden layer for the neural network.\n\t\t\t\t\t There can be multiple hidden layers. For every hidden layer, use this option.\n"; + std::cout << "\t:option \'-o\': integer \t - \t The size of the output layer for the neural network.\n"; + exit(8); +} diff --git a/neural-network/Loss.cpp b/src/loss.cpp similarity index 94% rename from neural-network/Loss.cpp rename to src/loss.cpp index 60db55d..758bb05 100644 --- a/neural-network/Loss.cpp +++ b/src/loss.cpp @@ -1,27 +1,27 @@ - -#include "Neural.hpp" - -/** - * Computes the model's MSE loss. - * - * @param[in] Y the expected output. This is the ground truth given the same input - * @param[in] dim the size of the output layer and therefore the size of the `Y` placeholder - * - * @return the total loss based on the model's predictions on a given sample and the corresponding (expected) output - * - * @note The given vectors must be of the same dimensions. - * - * @note Although passed by reference, the `Y` placeholder is not altered. - */ - -double nn::mse_loss(double* (&Y), int dim) -{ - double l = 0.0; /// Initializes loss variable (accumulator) -#pragma omp simd reduction(+ : l) - for (int i = 0; i < dim; i += 1) - { - l += (1.0 / 2.0) * (Y[i] - a[layers.size() - 1][i]) * (Y[i] - a[layers.size() - 1][i]); - } - - return l; -} + +#include "neural.hpp" + +/** + * Computes the model's MSE loss. + * + * @param[in] Y the expected output. This is the ground truth given the same input + * @param[in] dim the size of the output layer and therefore the size of the `Y` placeholder + * + * @return the total loss based on the model's predictions on a given sample and the corresponding (expected) output + * + * @note The given vectors must be of the same dimensions. + * + * @note Although passed by reference, the `Y` placeholder is not altered. + */ + +double nn::mse_loss(double* (&Y), int dim) +{ + double l = 0.0; /// Initializes loss variable (accumulator) +#pragma omp simd reduction(+ : l) + for (int i = 0; i < dim; i += 1) + { + l += (1.0 / 2.0) * (Y[i] - a[layers.size() - 1][i]) * (Y[i] - a[layers.size() - 1][i]); + } + + return l; +} diff --git a/neural-network/Optimize.cpp b/src/optimize.cpp similarity index 95% rename from neural-network/Optimize.cpp rename to src/optimize.cpp index 4b696f0..2bc50a2 100644 --- a/neural-network/Optimize.cpp +++ b/src/optimize.cpp @@ -1,91 +1,91 @@ - -#include "Neural.hpp" - -/** - * Computes each neuron's error of a given neural network. - - * @param[in, out] Y the expected output of the model for a given input - * - * @note Although passed by reference, the `Y` placeholder is not altered. - * - * @note Using the `REGISTER` variable, we can take advantage of the `reduction()` routine. - * The `delta` container is not contiguous and therefore cannot be 'reduced'. - * - * @note Although there was no need for the purposes of the project to compute the error of more - * than 1 (one) hidden layers, there is a loop that does exactly that, for completeness. - */ -void nn::back_propagation(double* (&Y)) -{ -#pragma omp parallel for num_threads(N_THREADS) - for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) - { - delta[layers.size() - 2][neuron] = (a[layers.size() - 1][neuron] - Y[neuron]) * sig_derivative(a[layers.size() - 1][neuron]); /// Computes the error of the neurons in the last layer - } -#pragma omp parallel for num_threads(N_THREADS) - for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) - { - double REGISTER = 0.0; -#pragma omp simd reduction(+ : REGISTER) - for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) - { - REGISTER += weights[layers.size() - 2][neuron][synapse] * delta[layers.size() - 2][neuron]; /// Computes the first factor of the error of neurons for the last *hidden* layer - } - delta[layers.size() - 3][synapse] = REGISTER; - } - -#pragma omp parallel for num_threads(N_THREADS) - for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) - { - delta[layers.size() - 3][synapse] = delta[layers.size() - 3][synapse] * sig_derivative(a[layers.size() - 2][synapse]); /// Computes the total neuron error for each neuron in the last *hidden* layer - } - - for (int layer = 2; layer < layers.size() - 1; layer += 1) /// Computes the error for neurons in the remaining hidden layers using the same method - { -#pragma omp parallel for num_threads(N_THREADS) - for (int synapse = 0; synapse < layers[layers.size() - layer - 1]; synapse += 1) - { - double REGISTER = 0.0; -#pragma omp simd reduction(+ : REGISTER) - for (int neuron = 0; neuron < layers[layers.size() - layer] - 1; neuron += 1) /// There is no synapse between the bias at layer `l` and any neuron at layer `l - 1` - { - REGISTER += weights[layers.size() - layer - 1][neuron][synapse] * delta[layers.size() - layer - 1][neuron]; - } - delta[layers.size() - layer - 2][synapse] = REGISTER; - } - -#pragma omp parallel for num_threads(N_THREADS) - for (int synapse = 0; synapse < layers[layers.size() - layer - 1]; synapse += 1) - { - delta[layers.size() - layer - 2][synapse] = delta[layers.size() - layer - 2][synapse] * sig_derivative(a[layers.size() - layer - 1][synapse]); - } /// Deallocates memory space requested for the `REGISTER` container - } -} - -/** - * Optimizes weights by subtracting the precomputed error corresponding to each neuron pair (synapse). - */ -void nn::optimize(void) -{ -#pragma omp parallel for num_threads(N_THREADS) - for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) /// Loops through all neurons in the last layer - { -#pragma omp simd - for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) /// Loops through all neurons in the last *hidden* layer - { - weights[layers.size() - 2][neuron][synapse] -= LEARNING_RATE * delta[layers.size() - 2][neuron] * a[layers.size() - 2][synapse]; /// Optimizes weights between those synapses - } - } - - for (int layer = 2; layer < layers.size(); layer += 1) /// Loops through all the other layers - { -#pragma omp parallel for num_threads(N_THREADS) - for (int neuron = 0; neuron < layers[layers.size() - layer] - 1; neuron += 1) - { -#pragma omp simd - for (int synapse = 0; synapse < layers[layers.size() - layer - 1]; synapse += 1) /// Uses the same method to optimize the rest of the model's synapses - { - weights[layers.size() - layer - 1][neuron][synapse] -= LEARNING_RATE * delta[layers.size() - layer - 1][neuron] * a[layers.size() - layer - 1][synapse]; - } - } - } -} + +#include "neural.hpp" + +/** + * Computes each neuron's error of a given neural network. + + * @param[in, out] Y the expected output of the model for a given input + * + * @note Although passed by reference, the `Y` placeholder is not altered. + * + * @note Using the `REGISTER` variable, we can take advantage of the `reduction()` routine. + * The `delta` container is not contiguous and therefore cannot be 'reduced'. + * + * @note Although there was no need for the purposes of the project to compute the error of more + * than 1 (one) hidden layers, there is a loop that does exactly that, for completeness. + */ +void nn::back_propagation(double* (&Y)) +{ +#pragma omp parallel for num_threads(N_THREADS) + for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) + { + delta[layers.size() - 2][neuron] = (a[layers.size() - 1][neuron] - Y[neuron]) * sig_derivative(a[layers.size() - 1][neuron]); /// Computes the error of the neurons in the last layer + } +#pragma omp parallel for num_threads(N_THREADS) + for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) + { + double REGISTER = 0.0; +#pragma omp simd reduction(+ : REGISTER) + for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) + { + REGISTER += weights[layers.size() - 2][neuron][synapse] * delta[layers.size() - 2][neuron]; /// Computes the first factor of the error of neurons for the last *hidden* layer + } + delta[layers.size() - 3][synapse] = REGISTER; + } + +#pragma omp parallel for num_threads(N_THREADS) + for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) + { + delta[layers.size() - 3][synapse] = delta[layers.size() - 3][synapse] * sig_derivative(a[layers.size() - 2][synapse]); /// Computes the total neuron error for each neuron in the last *hidden* layer + } + + for (int layer = 2; layer < layers.size() - 1; layer += 1) /// Computes the error for neurons in the remaining hidden layers using the same method + { +#pragma omp parallel for num_threads(N_THREADS) + for (int synapse = 0; synapse < layers[layers.size() - layer - 1]; synapse += 1) + { + double REGISTER = 0.0; +#pragma omp simd reduction(+ : REGISTER) + for (int neuron = 0; neuron < layers[layers.size() - layer] - 1; neuron += 1) /// There is no synapse between the bias at layer `l` and any neuron at layer `l - 1` + { + REGISTER += weights[layers.size() - layer - 1][neuron][synapse] * delta[layers.size() - layer - 1][neuron]; + } + delta[layers.size() - layer - 2][synapse] = REGISTER; + } + +#pragma omp parallel for num_threads(N_THREADS) + for (int synapse = 0; synapse < layers[layers.size() - layer - 1]; synapse += 1) + { + delta[layers.size() - layer - 2][synapse] = delta[layers.size() - layer - 2][synapse] * sig_derivative(a[layers.size() - layer - 1][synapse]); + } /// Deallocates memory space requested for the `REGISTER` container + } +} + +/** + * Optimizes weights by subtracting the precomputed error corresponding to each neuron pair (synapse). + */ +void nn::optimize(void) +{ +#pragma omp parallel for num_threads(N_THREADS) + for (int neuron = 0; neuron < layers[layers.size() - 1]; neuron += 1) /// Loops through all neurons in the last layer + { +#pragma omp simd + for (int synapse = 0; synapse < layers[layers.size() - 2]; synapse += 1) /// Loops through all neurons in the last *hidden* layer + { + weights[layers.size() - 2][neuron][synapse] -= LEARNING_RATE * delta[layers.size() - 2][neuron] * a[layers.size() - 2][synapse]; /// Optimizes weights between those synapses + } + } + + for (int layer = 2; layer < layers.size(); layer += 1) /// Loops through all the other layers + { +#pragma omp parallel for num_threads(N_THREADS) + for (int neuron = 0; neuron < layers[layers.size() - layer] - 1; neuron += 1) + { +#pragma omp simd + for (int synapse = 0; synapse < layers[layers.size() - layer - 1]; synapse += 1) /// Uses the same method to optimize the rest of the model's synapses + { + weights[layers.size() - layer - 1][neuron][synapse] -= LEARNING_RATE * delta[layers.size() - layer - 1][neuron] * a[layers.size() - layer - 1][synapse]; + } + } + } +} diff --git a/neural-network/Parser.cpp b/src/parser.cpp similarity index 96% rename from neural-network/Parser.cpp rename to src/parser.cpp index 2409f1a..5b118d5 100644 --- a/neural-network/Parser.cpp +++ b/src/parser.cpp @@ -1,56 +1,56 @@ - -#include "Parser.hpp" - -/** - * Converts a string argument to integer. - * - * @param[in] argv the string to convert - * - * @return the integer that corresponds to that string - * - * @note Visual studio has marked the `sscanf()` as deprecated routine. - * However, this is just for Windows OS. - */ -int parse_integer(char* argv) -{ - int intvar; - - if (sscanf(argv, "%d", &intvar) != 1) - { - fprintf(stderr, "error - not an integer"); - } - - return intvar; -} - -/** - * Parses all user arguments and initializes all necessary project attributes, such as the model's hyperparameters. - * - * @param[in] argc the number of user arguments - * @param[in] argv the vector of the user arguments - * @param[in, out] vec the container to be given the neural network's structure - */ -void parse_arguments(int argc, char* argv[], std::vector& vec) -{ - char* filename = argv[0]; - - while ((argc > 1) && (argv[1][0] == '-')) /// Loops through all arguments - { - switch (argv[1][1]) /// Stops when there are no more arguments - { - case 'i': /// '-i' option: This is used to give an input size for the first layer of the model - vec.push_back(parse_integer(&argv[2][0]) + 1); - break; - case 'h': /// '-h' option: This is used to give the size of a hidden layer of the model - vec.push_back(parse_integer(&argv[2][0]) + 1); /// There can be more than one hidden layers, and all have to be initialized using the '-h' option - break; - case 'o': /// '-o' option: This is used to give an output size for the last layer of the model - vec.push_back(parse_integer(&argv[2][0])); - break; - default: - usage(filename); /// If given option is invalid, the program prints the usage and terminates execution - } - argv += 2; - argc -= 2; - } -} + +#include "parser.hpp" + +/** + * Converts a string argument to integer. + * + * @param[in] argv the string to convert + * + * @return the integer that corresponds to that string + * + * @note Visual studio has marked the `sscanf()` as deprecated routine. + * However, this is just for Windows OS. + */ +int parse_integer(char* argv) +{ + int intvar; + + if (sscanf(argv, "%d", &intvar) != 1) + { + fprintf(stderr, "error - not an integer"); + } + + return intvar; +} + +/** + * Parses all user arguments and initializes all necessary project attributes, such as the model's hyperparameters. + * + * @param[in] argc the number of user arguments + * @param[in] argv the vector of the user arguments + * @param[in, out] vec the container to be given the neural network's structure + */ +void parse_arguments(int argc, char* argv[], std::vector& vec) +{ + char* filename = argv[0]; + + while ((argc > 1) && (argv[1][0] == '-')) /// Loops through all arguments + { + switch (argv[1][1]) /// Stops when there are no more arguments + { + case 'i': /// '-i' option: This is used to give an input size for the first layer of the model + vec.push_back(parse_integer(&argv[2][0]) + 1); + break; + case 'h': /// '-h' option: This is used to give the size of a hidden layer of the model + vec.push_back(parse_integer(&argv[2][0]) + 1); /// There can be more than one hidden layers, and all have to be initialized using the '-h' option + break; + case 'o': /// '-o' option: This is used to give an output size for the last layer of the model + vec.push_back(parse_integer(&argv[2][0])); + break; + default: + usage(filename); /// If given option is invalid, the program prints the usage and terminates execution + } + argv += 2; + argc -= 2; + } +} diff --git a/neural-network/Utilities.cpp b/src/utilities.cpp similarity index 96% rename from neural-network/Utilities.cpp rename to src/utilities.cpp index 5f5d367..7d4266d 100644 --- a/neural-network/Utilities.cpp +++ b/src/utilities.cpp @@ -1,214 +1,214 @@ - -#include "Neural.hpp" - -/** - * This function pools the element with the maximum value from the - * predictions vector. - * - * @param[in, out] y_pred the predictions vector - * - * @return the index of the element with the maximum value - * - * @note Although passed by reference, `y_pred` is not altered. - */ -int nn::get_label(double* (&y_pred)) -{ - int label; - double max_val = -2.0; - - for (int i = 0; i < layers[layers.size() - 1]; i += 1) - { - if (y_pred[i] > max_val) - { - max_val = y_pred[i]; - label = i; - } - } - - return label; -} - -/** - * Uses model to make predictions using custom inputs. - * - * @param[in, out] X the input to be given to the model - * - * @return the predicted class with respect to the given input - * - * @note Although passed by reference, the `X` placeholder is not altered. - */ -int nn::predict(double* (&X)) -{ - zero_grad(X); - forward(); - return get_label(a[layers.size() - 1]); -} - -/** - * Initializes model structure. - * - * @param[in] l the vector containing the model's structure - */ -void nn::set_layers(const std::vector& l) -{ - for (auto& elem : l) - { - layers.push_back(elem); - } -} - -/** - * Allocates memory space for the dynamic matrix that contains the neurons' unfiltered value. - * - * @param[in, out] l the neural network layer structure vector - * - * @note The `z` container for each neuron `i` in layer a `u` holds the sum given by the - * formula: - * \sum_j^L{synapse_{i, j} * value_{j}}, where synapse is the numerical weight - * of the synapse between neuron `i`, `j` and the value of `j` is the filtered - * output of that neuron and `L` is the number of neurons found in layer `u - 1`. - * The filter refers to the activation function used to 'activate' the neurons - * in the neural network. - */ -void nn::set_z(const std::vector& l) -{ - z = new double* [l.size()]; - for (int i = 0; i < l.size(); i += 1) - { - z[i] = new double[l[i]]; - } -} - -/** - * Allocates memory space for the dynamic matrix that contains the neurons' filtered value. - * - * @param[in, out] l the neural network layer structure vector - * - * @note The `a` container for each neuron `i` in layer `l` holds the sum given by the - * formula: - * f{(z_i)}, \forall i \in `l`, where f is the chosen activation function for every - * neuron i the model. - */ -void nn::set_a(const std::vector& l) -{ - a = new double* [l.size()]; - for (int i = 0; i < l.size(); i += 1) - { - a[i] = new double[l[i]]; - } -} - -/** - * Allocates memory space for the dynamic matrix that contains the neurons' error. - * - * @param[in, out] l the neural network layer structure vector - */ -void nn::set_delta(const std::vector& l) -{ - delta = new double* [l.size() - 1]; - for (int i = 1; i < l.size(); i += 1) - { - delta[i - 1] = new double[l[i]]; - } -} - -/** - * Sets model's weights of synapses. - * - * @param[in] l the neural network layer structure vector - * @param[in] min the minimum weight of a synapse - * @param[in] max the maximum weight of a synapse - */ -void nn::set_weights(const std::vector& l, const double min, const double max) -{ - std::random_device rd; /// Initializes non-deterministic random generator - std::mt19937 gen(rd()); /// Seeds mersenne twister - std::uniform_real_distribution<> dist(min, max); /// Distribute results between `min` and `max` inclusive - - weights = new double** [l.size() - 1]; /// Allocates memory for the weights container - for (int i = 1; i < l.size() - 1; i += 1) - { - weights[i - 1] = new double* [l[i] - 1]; /// Allocates memory for the weights of a layer in a neural network - for (int j = 0; j < l[i] - 1; j += 1) - { - weights[i - 1][j] = new double[l[i - 1]]; /// Allocates memory for the weights of each neuron in a layer - for (int k = 0; k < l[i - 1]; k += 1) - { - weights[i - 1][j][k] = dist(gen); /// Uses random generator to initialize synapse - } - } - } - weights[l.size() - 2] = new double* [l[l.size() - 1]]; /// Initializes weights in the output layer - for (int j = 0; j < l[l.size() - 1]; j += 1) /// There is no bias in the output layer - { - weights[l.size() - 2][j] = new double[l[l.size() - 2]]; /// Allocates memory for the weights of each neuron in the output layer - for (int k = 0; k < l[l.size() - 2]; k += 1) - { - weights[l.size() - 2][j][k] = dist(gen); /// Uses random generator to initialize synapse - } - } -} - -void nn::compile(const std::vector& l, const double min, const double max) -{ - set_layers(l); - set_z(l); - set_a(l); - set_delta(l); - set_weights(l, min, max); -} - -/** - * Clears the past values computed created during feed forward and/or back propagation processes. - * - * @param[in, out] X a vector that has been initialized with a random sample from the training data subset - * - * @note This function is called upon both training and evaluation. That's why it also clears past values of the `delta` container. - */ -void nn::zero_grad(double* (&X)) -{ - for (int j = 0; j < layers[0] - 1; j += 1) /// Prepare - initialize input layer - { - z[0][j] = X[j]; - a[0][j] = X[j]; - } - z[0][layers[0] - 1] = 1.0; - a[0][layers[0] - 1] = 1.0; - - for (int i = 1; i < layers.size() - 1; i += 1) /// Prepare - initialize hidden layers - { - for (int j = 0; j < layers[i] - 1; j += 1) - { - z[i][j] = 0.0; - a[i][j] = 0.0; - delta[i - 1][j] = 0.0; - } - z[i][layers[i] - 1] = 1.0; - a[i][layers[i] - 1] = 1.0; - delta[i - 1][layers[i] - 1] = 0.0; - } - - for (int j = 0; j < layers[layers.size() - 1]; j += 1) /// Prepare - initialize output layer - { - z[layers.size() - 1][j] = 0.0; - a[layers.size() - 1][j] = 0.0; - delta[layers.size() - 2][j] = 0.0; - } -} - -/** - * Prints Neural Network layer structure. - */ -void nn::summary(void) -{ - int l = 0; - - std::string s(CLI_WINDOW_WIDTH + 10, '-'); - - std::cout << "\n\nNeural Network Summary:\t\t[f := Sigmoid]\n" << s << std::endl; - - for (auto& elem : layers) - { - std::cout << "Layer [" << ++l << "]\t" << std::setw(4) << elem << " neurons\n"; - } -} + +#include "neural.hpp" + +/** + * This function pools the element with the maximum value from the + * predictions vector. + * + * @param[in, out] y_pred the predictions vector + * + * @return the index of the element with the maximum value + * + * @note Although passed by reference, `y_pred` is not altered. + */ +int nn::get_label(double* (&y_pred)) +{ + int label; + double max_val = -2.0; + + for (int i = 0; i < layers[layers.size() - 1]; i += 1) + { + if (y_pred[i] > max_val) + { + max_val = y_pred[i]; + label = i; + } + } + + return label; +} + +/** + * Uses model to make predictions using custom inputs. + * + * @param[in, out] X the input to be given to the model + * + * @return the predicted class with respect to the given input + * + * @note Although passed by reference, the `X` placeholder is not altered. + */ +int nn::predict(double* (&X)) +{ + zero_grad(X); + forward(); + return get_label(a[layers.size() - 1]); +} + +/** + * Initializes model structure. + * + * @param[in] l the vector containing the model's structure + */ +void nn::set_layers(const std::vector& l) +{ + for (auto& elem : l) + { + layers.push_back(elem); + } +} + +/** + * Allocates memory space for the dynamic matrix that contains the neurons' unfiltered value. + * + * @param[in, out] l the neural network layer structure vector + * + * @note The `z` container for each neuron `i` in layer a `u` holds the sum given by the + * formula: + * \sum_j^L{synapse_{i, j} * value_{j}}, where synapse is the numerical weight + * of the synapse between neuron `i`, `j` and the value of `j` is the filtered + * output of that neuron and `L` is the number of neurons found in layer `u - 1`. + * The filter refers to the activation function used to 'activate' the neurons + * in the neural network. + */ +void nn::set_z(const std::vector& l) +{ + z = new double* [l.size()]; + for (int i = 0; i < l.size(); i += 1) + { + z[i] = new double[l[i]]; + } +} + +/** + * Allocates memory space for the dynamic matrix that contains the neurons' filtered value. + * + * @param[in, out] l the neural network layer structure vector + * + * @note The `a` container for each neuron `i` in layer `l` holds the sum given by the + * formula: + * f{(z_i)}, \forall i \in `l`, where f is the chosen activation function for every + * neuron i the model. + */ +void nn::set_a(const std::vector& l) +{ + a = new double* [l.size()]; + for (int i = 0; i < l.size(); i += 1) + { + a[i] = new double[l[i]]; + } +} + +/** + * Allocates memory space for the dynamic matrix that contains the neurons' error. + * + * @param[in, out] l the neural network layer structure vector + */ +void nn::set_delta(const std::vector& l) +{ + delta = new double* [l.size() - 1]; + for (int i = 1; i < l.size(); i += 1) + { + delta[i - 1] = new double[l[i]]; + } +} + +/** + * Sets model's weights of synapses. + * + * @param[in] l the neural network layer structure vector + * @param[in] min the minimum weight of a synapse + * @param[in] max the maximum weight of a synapse + */ +void nn::set_weights(const std::vector& l, const double min, const double max) +{ + std::random_device rd; /// Initializes non-deterministic random generator + std::mt19937 gen(rd()); /// Seeds mersenne twister + std::uniform_real_distribution<> dist(min, max); /// Distribute results between `min` and `max` inclusive + + weights = new double** [l.size() - 1]; /// Allocates memory for the weights container + for (int i = 1; i < l.size() - 1; i += 1) + { + weights[i - 1] = new double* [l[i] - 1]; /// Allocates memory for the weights of a layer in a neural network + for (int j = 0; j < l[i] - 1; j += 1) + { + weights[i - 1][j] = new double[l[i - 1]]; /// Allocates memory for the weights of each neuron in a layer + for (int k = 0; k < l[i - 1]; k += 1) + { + weights[i - 1][j][k] = dist(gen); /// Uses random generator to initialize synapse + } + } + } + weights[l.size() - 2] = new double* [l[l.size() - 1]]; /// Initializes weights in the output layer + for (int j = 0; j < l[l.size() - 1]; j += 1) /// There is no bias in the output layer + { + weights[l.size() - 2][j] = new double[l[l.size() - 2]]; /// Allocates memory for the weights of each neuron in the output layer + for (int k = 0; k < l[l.size() - 2]; k += 1) + { + weights[l.size() - 2][j][k] = dist(gen); /// Uses random generator to initialize synapse + } + } +} + +void nn::compile(const std::vector& l, const double min, const double max) +{ + set_layers(l); + set_z(l); + set_a(l); + set_delta(l); + set_weights(l, min, max); +} + +/** + * Clears the past values computed created during feed forward and/or back propagation processes. + * + * @param[in, out] X a vector that has been initialized with a random sample from the training data subset + * + * @note This function is called upon both training and evaluation. That's why it also clears past values of the `delta` container. + */ +void nn::zero_grad(double* (&X)) +{ + for (int j = 0; j < layers[0] - 1; j += 1) /// Prepare - initialize input layer + { + z[0][j] = X[j]; + a[0][j] = X[j]; + } + z[0][layers[0] - 1] = 1.0; + a[0][layers[0] - 1] = 1.0; + + for (int i = 1; i < layers.size() - 1; i += 1) /// Prepare - initialize hidden layers + { + for (int j = 0; j < layers[i] - 1; j += 1) + { + z[i][j] = 0.0; + a[i][j] = 0.0; + delta[i - 1][j] = 0.0; + } + z[i][layers[i] - 1] = 1.0; + a[i][layers[i] - 1] = 1.0; + delta[i - 1][layers[i] - 1] = 0.0; + } + + for (int j = 0; j < layers[layers.size() - 1]; j += 1) /// Prepare - initialize output layer + { + z[layers.size() - 1][j] = 0.0; + a[layers.size() - 1][j] = 0.0; + delta[layers.size() - 2][j] = 0.0; + } +} + +/** + * Prints Neural Network layer structure. + */ +void nn::summary(void) +{ + int l = 0; + + std::string s(CLI_WINDOW_WIDTH + 10, '-'); + + std::cout << "\n\nNeural Network Summary:\t\t[f := Sigmoid]\n" << s << std::endl; + + for (auto& elem : layers) + { + std::cout << "Layer [" << ++l << "]\t" << std::setw(4) << elem << " neurons\n"; + } +}

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