try adding diagrams
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Andre Henriques 2023-11-30 11:47:09 +00:00
parent 9c5e178e05
commit 8bfecc95d9
7 changed files with 230 additions and 19 deletions

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@ -8,7 +8,14 @@ steps:
commands: commands:
- bash linting.sh - bash linting.sh
- name: Build UPDS-1 - name: Build diagrams
- commands:
- cd diagrams
- ls | grep d2 | xargs -I{} d2 --layout=elk {}
- mv *.svg '../images for report'
- cd -
- name: Build Project synopsis
commands: commands:
- cd projectsynopsis - cd projectsynopsis
- pdflatex project-synopsis.tex - pdflatex project-synopsis.tex

1
diagrams/.gitignore vendored Normal file
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@ -0,0 +1 @@
*.svg

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indata: "Input data" {
shape: cylinder
}
model_generation: Model Generation {
hidden_layers_generator: Hidden Layers Model Generator
hidden_layers_generator<->generator: Request/Accept Model
head_generator: Head Models Generator {
_.head_generator->model1: Create
_.head_generator->model2: Create
_.head_generator->modeln: Create
model1
model2
modeln: "Model..."
model1<->_.generator: Request/Accept Model
model2<->_.generator: Request/Accept Model
modeln<->_.generator: Request/Accept Model
model1->_.head_generator: Accept Model
model2->_.head_generator: Accept Model
modeln->_.head_generator: Accept Model
}
generator: Generator {
_.generator->model_search: Start
model_search: Model Search {
database_search: Database search
autoML: Automl
}
model_search->model_training: Propose model
model_training->model_search: Reject model and request new one
model_training: Model Traning {
_.model_training->node: Start Training
node: Node
_.model_training<-node: End Training
}
model_training->_.generator: Accept Model
}
}
node_manager: Node_Manager {
node1
node2
noden: "node..."
node_manager->node1: Manage
node_manager->node2: Manage
node_manager->noden: Manage
}
model_generation.generator.model_training.node<->node_manager: Request/Gives node to train
model_database: Model database {
shape: cylinder
}
model_runner: Model Runner {
node: Node
headless: Obatin Headless Model
_.model_runner->headless: Start
headless<->_.model_database: Request/Get Model
headless<->node: Run/Result
headless->model_search: Results
model_search: Model Search {
}
model_search<->_.model_database: Request Head Models
head_model: Head Model
model_search->head_model: Obtain
head_model<->node: Run/Result
head_model->model_search: Unsatatisfary results, request new model
head_model->results_cache: Unsatatisfary results, save results
results_cache: Results Cache
combine: Combine
head_model->combine: Satisfary Results/No more options
combine<-results_cache: Cached results Results
combine->_.model_runner: Results
}
model_runner.node<->node_manager: Request/Gives node to run model
User.shape: Person
User->indata: Uploads data
User->model_generation: Requests Model
User->model_database: Manages Models
User->model_runner: Request image for classification
model_runner->User: Give class of image
model_generation.generator <-> indata: Requests Data
model_generation->model_generation.hidden_layers_generator: Start
model_generation.hidden_layers_generator->model_generation.head_generator: Strip head and give base model to Head Generator
model_generation.head_generator->model_database: Save hidden layers model and heads

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diagrams/simple_models.d2 Normal file
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indata: "Input data" {
shape: cylinder
}
model_generation: Model Generation {
_.generator->model_search: Start
model_search: Model Search {
database_search: Database search
autoML: Automl
}
model_search->model_training: Propose model
model_training->model_search: Reject model and request new one
model_training: Model Traning {
_.model_training->node: Start Training
node: Node
_.model_training<-node: End Training
}
model_training->_.generator: Accept Model
}
node_manager: Node_Manager {
node1
node2
noden: "node..."
node_manager->node1: Manage
node_manager->node2: Manage
node_manager->noden: Manage
}
model_generation.generator.model_training.node<->node_manager: Request/Gives node to train
model_database: Model database {
shape: cylinder
}
model_runner: Model Runner {
node: Node
model: Model
model<->_.model_database: Request/Get Model
model<->node: Run/Result
node->_.model_runner: Results
}
model_runner.node<->node_manager: Request/Gives node to run model
User.shape: Person
User->indata: Uploads data
User->model_generation: Requests Model
User->model_database: Manages Models
User->model_runner: Request image for classification
model_runner->User: Give class of image
model_generation <-> indata: Requests Data

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@ -1,13 +1,13 @@
@online{google-vision-api, @online{google-vision-api,
author ={Google}, author ={Google},
title ={Vision AI | Google Cloud}, title ={Vision {AI} | Google Cloud},
year ={2023}, year ={2023},
url ={https://cloud.google.com/vision?hl=en} url ={https://cloud.google.com/vision?hl=en}
} }
@article{amazon-rekognition, @article{amazon-rekognition,
author ={Amazon}, author ={Amazon},
title ={Image Recognition Software - ML Image \& Video Analysis - Amazon Rekognition - AWS}, title ={Image Recognition Software - {ML} Image \& Video Analysis - Amazon Rekognition - AWS},
year ={2023}, year ={2023},
url ={https://aws.amazon.com/rekognition/} url ={https://aws.amazon.com/rekognition/}
} }

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@ -58,39 +58,38 @@
\section{Introduction} \section{Introduction}
% This section should contain an introduction to the problem aims and objectives (0.5 page) % This section should contain an introduction to the problem aims and objectives (0.5 page)
Currently, there are many classification tasks that are being done manually. These tasks could be done more effectively if there was tooling that would allow the easy creation of classification models, without the knowledge of data analysis and machine learning models creation. Currently, there are many classification tasks that are being done manually. These tasks could be done more effectively if there was tooling that would allow the easy creation of classification models, without the knowledge of data analysis and machine learning models creation.
The aim of this project is to create a classification service that has 0 requires zero user knowledge about machine learning, image classification or data analysis. The aim of this project is to create a classification service that requires zero user knowledge about machine learning, image classification or data analysis.
The system should allow the user to create a reasonable accurate model that can satisfy the users' need. The system should allow the user to create a reasonable accurate model that can satisfy the users' need.
The system should also allow the user to create expandable models; models where classes can be added after the model has been created. The system should also allow the user to create expandable models; models where classes can be added after the model has been created.
\subsection{Aims} \subsection{Project Aim}
The project aims to create a platform where users can create different types of classification models without the users having any knowledge of image classification. The project aims to create a platform where users can create different types of classification models without the users having any knowledge of image classification.
\subsection{Objectives} \subsection{Project Objectives}
This project's primary objectives are to: This project's primary objectives are to:
\begin{itemize} \begin{itemize}
\item Create platform where the users can create and manage their models. \item Create platform where the users can create and manage their models.
\item Create a system to automatically create and train.
\item Create a system to automatically create and train models. \item Create a system to automatically create and train models.
\item Create a system to automatically expand and reduce models without fully retraining the models. \item Create a system to automatically expand and reduce models without fully retraining the models.
\item Create an API so that users can interact programatically with the system. \item Create an API so that users can interact programatically with the system.
\end{itemize} \end{itemize}
This project extended objectives are to: This project extended objectives are to:
\begin{itemize} \begin{itemize}
\item Create a system to automatically to merge modules to increase efficiency \item Create a system to automatically to merge modules to increase efficiency.
\item Create a system to distribute the load of training the model's among multiple services. \item Create a system to distribute the load of training the model's among multiple services.
\end{itemize} \end{itemize}
\section{Literature and Techincal Review} \section{Literature and Techincal Review}
% 1 page of background and literature review. Here you will need to references things. Gamal et al.~\cite{gamal} introduce the concept of \ldots % 1 page of background and literature review. Here you will need to references things. Gamal et al.~\cite{gamal} introduce the concept of \ldots
\subsection{Alternatives to my Project} \subsection{Alternatives to my Project}
There currently exist systems that do image classification, like Google Vision AI\cite{google-vision-api}, and Amazon's Rekoginition\cite{amazon-rekognition}. There currently exist systems that do image classification, like Google Vision AI \cite{google-vision-api}, and Amazon's Rekoginition \cite{amazon-rekognition}.
Their tools, while providing similar services to what my project is supposed to do, it mostly focusses on general image classification rather than specific image classification, i.e. Car vs Boat, vs, Car model X vs Car model Y. Their tools, while providing similar services to what my project is supposed to do, it mostly focusses on general image classification rather than specific image classification, i.e. Car vs Boat, vs, Car model X vs Car model Y.
\subsection{Creation Models} \subsection{Creation Models}
The models that I will be creating will be Convolutional Neural Network(CNN)\cite{lecun1989handwritten,fukushima1980neocognitron}. The models that I will be creating will be Convolutional Neural Network(CNN) \cite{lecun1989handwritten,fukushima1980neocognitron}.
The system will be creating two types of models that cannot be expanded and models that can be expanded. For the models that can be expanded, see the section about expandable models. The system will be creating two types of models that cannot be expanded and models that can be expanded. For the models that can be expanded, see the section about expandable models.
The models that cannot be expanded will use a simple convolution blocks, with a similar structure as the AlexNet\cite{krizhevsky2012imagenet} ones, as the basis for the model. The size of the model will be controlled by the size of the input image, where bigger images will generate more deep and complex models. The models that cannot be expanded will use a simple convolution blocks, with a similar structure as the AlexNet \cite{krizhevsky2012imagenet} ones, as the basis for the model. The size of the model will be controlled by the size of the input image, where bigger images will generate more deep and complex models.
The models will be created using TensorFlow\cite{tensorflow2015-whitepaper} and Keras\cite{chollet2015keras}. These theologies are chosen since they are both robust and used in industry. The models will be created using TensorFlow \cite{tensorflow2015-whitepaper} and Keras \cite{chollet2015keras}. These theologies are chosen since they are both robust and used in industry.
\subsection{Expandable Models} \subsection{Expandable Models}
The current most used approach for expanding a CNN model is to retrain the model. This is done by, recreating an entire new model that does the new task, using the older model as a base for the new model\cite{amazon-rekognition}, or using a pretrained model as a base and training the last few layers. The current most used approach for expanding a CNN model is to retrain the model. This is done by, recreating an entire new model that does the new task, using the older model as a base for the new model\cite{amazon-rekognition}, or using a pretrained model as a base and training the last few layers.
@ -99,18 +98,18 @@
\section{Technical overview} \section{Technical overview}
% 1 page of overview. My approach is shown in Figure~\ref{fig:sample}. You can draw the diagram in powerpoint and save the picture % technological free overview
\subsection{Web Interface} \subsection{Web Interface}
The user will interact with the platform form via a web portal. The user will interact with the platform form via a web portal. % why the web portal
The web platform will be designed using HTML and a JavaScript library called HTMX\cite{htmx} for the reactivity that the pagers requires. The web platform will be designed using HTML and a JavaScript library called HTMX \cite{htmx} for the reactivity that the pagers requires.
The web server that will act as controller will be implemented using go\cite{go}, due to its ease of use. The web server that will act as controller will be implemented using go \cite{go}, due to its ease of use.
Go was chosen has the programming language used in the server due to its performance, i.e. \cite{node-to-go}, and ease of implementation. As compiled language go, outperforms other server technologies such as Node.js. Go was chosen has the programming language used in the server due to its performance, i.e. \cite{node-to-go}, and ease of implementation. As compiled language go, outperforms other server technologies such as Node.js.
Go also has easy support for C ABI, which might be needed if there is a need to interact with other tools that are implemented using C. Go also has easy support for C ABI, which might be needed if there is a need to interact with other tools that are implemented using C.
The web server will also interact with python to create models. Then to run the models, it will use the libraries that are available to run TensorFlow\cite{tensorflow2015-whitepaper} models for that in go. The web server will also interact with python to create models. Then to run the models, it will use the libraries that are available to run TensorFlow \cite{tensorflow2015-whitepaper} models for that in go.
\subsection{Creating Models} \subsection{Creating Models}
The models will be created using TensorFlow\cite{tensorflow2015-whitepaper}. The models will be created using TensorFlow \cite{tensorflow2015-whitepaper}.
TensorFlow was chosen because, when using frameworks like Keras\cite{chollet2015keras}, it allows the easy development of machine learning models with little code. While tools like PyTorch might provide more advanced control options for the model, like dynamic graphs, it comes at the cost of more complex python code. Since that code is generated by the go code, the more python that needs to be written, the more complex the overall program gets, which is not desirable. TensorFlow was chosen because, when using frameworks like Keras \cite{chollet2015keras}, it allows the easy development of machine learning models with little code. While tools like PyTorch might provide more advanced control options for the model, like dynamic graphs, it comes at the cost of more complex python code. Since that code is generated by the go code, the more python that needs to be written, the more complex the overall program gets, which is not desirable.
The original plan was to use go and TensorFlow, but the go library was lacking that ability. Therefore, I chose to use python to create the models. The original plan was to use go and TensorFlow, but the go library was lacking that ability. Therefore, I chose to use python to create the models.
The go server starts a new process, running python, that creates and trains the TensorFlow model. Once the training is done, the model is saved to disk which then can be loaded by the go TensorFlow library. The go server starts a new process, running python, that creates and trains the TensorFlow model. Once the training is done, the model is saved to disk which then can be loaded by the go TensorFlow library.
@ -122,6 +121,7 @@
\section{Workplan} \section{Workplan}
\subsection{Timeline} \subsection{Timeline}
% bold the headres
% The following work plan is what I will be using for the project is shown in Figure~\ref{fig:sample2}. % The following work plan is what I will be using for the project is shown in Figure~\ref{fig:sample2}.
\begin{tabular}{ |m{0.5\textwidth}|m{0.5\textwidth}| } \begin{tabular}{ |m{0.5\textwidth}|m{0.5\textwidth}| }
\hline \hline

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@ -56,6 +56,7 @@
\newpage \newpage
\section{Introduction} \section{Introduction}
\subsection{Motivation}
\newpage \newpage
\section{References} \section{References}