removed unnacenary parts and updaged generation

report/report.tex

@@ -97,7 +97,7 @@
 			There are also unsupervised learning methods that do not have a fixed number of classes. While this method would work as an expandable model method, it would not work for the purpose of this project. This project requires that the model has a specific set of labels which does not work with unsupervised learning which has unlabelled data. Some technics that are used for unsupervised learning might be useful in the process of creating expandable models.
 
 
-    \section{Technical overview}
+    \section{Problem analysis & design choices}
 
 		\subsection{Structure of the service}
 			The system needs to manage:
@@ -124,20 +124,24 @@
 
 		\subsection{Generation models}
 			The system requires the generation of models. Generating all models based on one single model would decrease the complexity of the system, but it would not guarantee success.
-
+			
 			The system needs to generate successful models, to achieve this, the system will be performing two approaches:
 			\begin{itemize}
-    			\item{Database search}
-    			\item{AutoML (secondary goal)}
+				\item{Database search}
+				\item{AutoML (secondary goal)}
 			\end{itemize}
 
 			The database search will consist of trying both previous models that are known to work to similar inputs, either by models that were previously generated by the system or known good models; base known architectures that are modified to match the size of the input images.
 
-			% TODO add diagrams
-
-			% TODO cite ResNet
 			An example of the first approach would be to try the ResNet model, while the second approach would be using the architecture of ResNet and configuring the architecture so it is more optimized for the input images.
 
+			AutoML approach would consist of using an AutoML system to generate new models that match the task at hand.
+
+			Since the AutoML approach would be more computational intensive, it would be less desirable to run. Therefore, the approach would be for the database search to happen first, where known possibly good models would be first tested. If a good model is found, then the search stops and if no model is found, the system would resort to AutoML to find a suitable model.
+
+			% TODO add diagram
+
+
 
 
 		
@@ -168,73 +172,6 @@
   %           The smaller models would all be all generated by the platform, this model's purpose would be actually classification.
   %           This approach would offer a lot of expandability, as it makes the addition of a new class as easy as creating a new small model.
 
-    \section{Workplan}
-    \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}.
-    \begin{tabular}{ |m{0.5\textwidth}|m{0.5\textwidth}| }
-		\hline
-        Month & Goals \\ 
-		\hline
-        September & \begin{itemize}
-                        \item Experimenting with web development frameworks.
-                        \item Started working on code development.
-                     \end{itemize} \\
-		\hline
-        October & \begin{itemize} 
-                        \item Starting working on Project Synopsis.
-                        \item Continue working on project development.
-                        \item Finish user management system and basic ui decisions.
-                        \item Finish data upload section of the website.
-                   \end{itemize} \\  
-		\hline
-        November & \begin{itemize} 
-                        \item Finish writing on Project Synopsis.
-                        \item Finish coding the basic model generation and training.
-                   \end{itemize} \\  
-		\hline
-        December & \begin{itemize} 
-                        \item Improve basic model generation.
-                   \end{itemize} \\  
-		\hline
-        January & \begin{itemize} 
-                        \item Add api support.
-                        \item Started working on the final report
-                   \end{itemize} \\  
-		\hline
-        Feburary & \begin{itemize} 
-                        \item Start working on expandable models generation
-                   \end{itemize} \\  
-		\hline
-        March & \begin{itemize} 
-                        \item Create systems to expand the expandable models and contract models
-                        \item Review draft submissions
-                   \end{itemize} \\  
-		\hline
-        April  & \begin{itemize} 
-                        \item Basic final report finish
-                        \item Create systems to expand and reduce expandable models
-                   \end{itemize} \\  
-		\hline
-        May  & \begin{itemize} 
-                        \item Finish and submit final report
-                \end{itemize} \\  
-		\hline
-    \end{tabular}
-
-    \subsection{Risks}
-    \begin{tabular}{ |m{0.5\textwidth}|m{0.5\textwidth}| }
-		\hline
-        Risk & Mitigation \\ 
-		\hline
-        Automatic model generation is not feasable & Other methods for creation of model can be used i.e. transfer learning, or semi-unsupervised learning\\
-		\hline
-        Easy model expancion is not feasble & Other methods of updating the models need to be implemented i.e. retratinng the entire model.\\
-		\hline
-        Not enough compute power to train models fast enough to develop the program & Free credits from online cloud providers like google or amazon\\
-		\hline
-    \end{tabular}
-
     \appendix
     \newpage