Project 2
Overview
The objective of this project is to gain experience building and training neural networks as multi layer perceptrons. In this project you will implement a fixed size two layer neural network and a set of generic network layers that can be used to build and train multi layer perceptrons.
The goals for this project are as follows:
- Implement the forward and backward pass for a two layer neural network.
- Generalize your network implementation to fully connected layers.
- Implement the forward and backward pass for a non-linear activation function (ReLU).
- Implement and understand the tradeoffs using network regularization techniques.
- Understand the characteristics of neural network based classification using the PROPS dataset.
Instructions
- Download the project starter code
- Unzip the starter code and upload to Google Drive
- Once unzipped, you should find a root directory titled ‘P2’. The ‘P2’ directory contains all starter code and files needed to complete this project. Please upload the ‘P2’ directory to your Google Drive.
- Open the
*.ipynb
and*.py
files and implement featuresWe recommend implementing the features in a Google Colab environment. The Colab development environment can be accessed by double-clicking on each
*.ipynb
and*.py
file within your Drive. Instructions for each feature are included in thetwo_layer_net.ipynb
andfully_connected_networks.ipynb
files.We suggest starting by implementing the required features as they appear in the
two_layer_net.ipynb
notebook, which can be thought of as part 1 of the project. Then work through thefully_connected_networks.ipynb
notebook as part 2 of the project.While working on the project, keep the following in mind:
- The notebook and the python file have clearly marked blocks where you are expected to write code. Do not write or modify any code outside of these blocks.
- Do not add or delete cells from the notebook. You may add new cells to perform scratch computations, but you should delete them before submitting your work.
- Run all cells, and do not clear out the outputs, before submitting. You will only get credit for code that has been run.
- To avoid experiencing Colab usage limits, save and close your notebooks once finished working.
- Submit your implementation for Autograder feedback
- Once you have implemented a portion of the required features, you may submit your work for feedback from the Autograder. To receive feedback, download your
*.ipynb
and*.py
files then upload them to the Project 2 Autograder. You may submit to the Autograder for feedback up to 5 times per day.
- Once you have implemented a portion of the required features, you may submit your work for feedback from the Autograder. To receive feedback, download your
- Download final implementation
- After implementing all features, save your work and download the completed
*.ipynb
and*.py
files. - The last cell of the
fully_connected_networks.ipynb
notebook will generate auniqueid_umid_P2.zip
file. The zip file should includetwo_layer_net.ipynb
,two_layer_net.py
,fully_connected_networks.ipynb
,fully_connected_networks.py
,nn_best_model.pt
,best_overfit_five_layer_net.pth
, andbest_two_layer_net.pth
for this assignment.
- After implementing all features, save your work and download the completed
- Submit your python and notebook files for grading
- Upload your files to the Autograder for grading consideration. Your highest score will be used for final grades.
Deadline
This project is due on Monday, October 14th at 11:59pm CT. We suggest starting as soon as possible.
Grading
This project will be graded by the Autograder. The project is worth a total of 110 points. You may submit to the Autograder for feedback up to 5 times per day.