辅导案例-EE4305

  • June 2, 2020

EE4305 – Mini Project – Description Introduction Please read the below summary for Mini Project before attempting. More detailed instructions are provided in the ​Google Colab Notebook​. In this project you will implement a neural network (NN) architecture from scratch. Building the neural network will give hands-on experience converting mathematical foundations of NN such as feed-forward and backpropagation algorithms into Python code. The project includes the implementation of sub-functions in NN such as loss functions, activation functions, and their derivatives. The project is divided into 3 phases: 1. Phase 1: Implement and test a baseline Neural Network 2. Phase 2: Integrate additional functionalities to the Neural Network model 3. Phase 3: Classify cancerous cells using Wisconsin Breast Cancer Dataset Following functions/ datasets will be used to evaluate your NN models: Classification Regression Real-world problem AND logic Sinusoidal function Classification of cancerous cells using ​Wisconsin Breast Cancer Dataset​. XOR logic Gaussian function Keypoints ● A basic code skeleton is provided for each phase. There are also plenty of hints included in the notebook to help you with code implementation. ● The base neural network is implemented as a ​class​. ● Comments are included in each function to explain what the function does ● Codes are also provided for observation of outcomes and visualization. ● For each phase present your observations and inferences in separate text cells. You may create additional visualizations to support your observations. For example, why and how a parameter affects the model? Does the model overfit? Is the model too big or too small? Online Resources ● You may refer to online resources to help implement the model. You are also welcome to completely rewrite the NN baseline class if you prefer to do so. ● Do not copy+paste contents. Your codes may be checked for plagiarism. ● Recommended sources: ​BP Algorithm​, ​Python Implementation 1​, ​Python Implementation 2​, ​Python Implementation 3​. Phases Please refer to the Google Colab Notebook for more details. Phase 1 (P1):​ Implement and test a baseline Neural Network ● Complete codes for the Neural Network Class: feedforward, backpropagation, activation, loss, derivatives, train, predict, evaluate. ● Explain your methodology ● Perform classification tests for AND/XOR logic. ● Perform regression tests for Sinusoidal/ Gaussian functions. ● Record results and observations Phase 2 (P2):​ Integrate additional functionalities to the Neural Network model ● Implement codes to improve NN class (ANY 3): regularization, mini-batch training, parameter initialization, additional layers, loss functions, activations ● Test your implementation using any dataset. ● Present your findings on how the functionality affects performance. Phase 3 (P3):​ Classify cancerous cells using Wisconsin Breast Cancer Dataset ● Evaluate the implemented NN Class on the breast cancer dataset ● Tune the model to and present the highest accuracy score obtained ● Explain your choice of model parameters Submission You will have three weeks to complete the Mini Project ​(Deadline: 15 June 2020)​. Open Google Colab notebook and make a copy from the menu (File -> save a copy in Drive). Save the file as: “​:EE4305-Mini Project.ipynb​”. For submission, save the file as a notebook using the menu (File -> download .ipynb) with the same file name and submit in the appropriate folder in LumiNUS (Mini Project -> Submissions) Instructions on using Google Colab Notebook Colab allows you to write and execute Python in your browser, with, Zero configuration required, Free access to GPUs, and Easy sharing If you have prior experience using Jupyter Notebooks, Colab is very similar. Please refer to the ​Help menu for FAQs. ​Tools menu provides useful information on commands and keyboard shortcuts. You may use the ​Table of contents menu on the left-hand side to easily navigate between sections. You may also refer to ​this introductory video​. NOTE: You will need a google account in order to access and save files under Google Collaboratory. You may use your existing accounts in Gmail or create a new one. Scoring P1 – Completion of NN base class implementation – Description of the Backpropagation algorithm – Successful run of Classification and Regression Tests – Observations and Inferences (Reporting) 3 1 6 4 P2 – Implementation of functions to improve model: # 3 Methods # 5 Methods 9 9 + 1 bonus P3 – Successful implementation of Breast Cancer Dataset – Observations and Inferences (Reporting) – Accuracy for Breast Cancer test data: # < 90% (or not obtained) # >=90%, < 95% # >=90%, < 95% # >=98% 3 2 0 1 2 2 + 1 bonus * The final score will be clipped to a maximum of 30 NOTE: Please report errors in the document (if any) to your module GA END OF DOCUMENT. WISH YOU ALL THE BEST

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