In this assignment, you will implement Fully-Connected Neural Networks and Convolutional Neural Networks for image classification models. The goals of this assignment are as follows:
- Understand Neural Networks and how they are arranged in layered architectures
- Understand and be able to implement modular backpropagation
- Implement various update rules used to optimize Neural Networks
- Implement Batch Normalization for training deep networks
- Implement Dropout to regularize networks
- Understand the architecture of Convolutional Neural Networks and get practice with training these models on data
This assignment is due on Friday, October 9th at 11:59pm EDT.
Q1: Fully-Connected Neural Network (40 points)
The notebook fully_connected_networks.ipynb will walk you through implementing Fully-Connected Neural Networks.
Q2: Convolutional Neural Network (60 points)
The notebook convolutional_networks.ipynb will walk you through implementing Convolutional Neural Networks.
1. Download the zipped assignment file
2. Unzip all and open the Colab file from the Drive
Once you unzip the downloaded content, please upload the folder to your Google Drive.
Then, open each
*.ipynb notebook file with Google Colab by right-clicking the *.ipynb file.
We recommend editing your
*.py file on Google Colab, set the ipython notebook and the code side by side.
For more information on using Colab, please see our Colab tutorial.
3. Work on the assignment
Work through the notebook, executing cells and writing code in
*.py, as indicated.
You can save your work, both
*.py, in Google Drive (click “File” -> “Save”) and resume later if you don’t want to complete it all at once.
While working on the assignment, 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.
4. Evaluate your implementation on Autograder
Once you want to evaluate your implementation,
please submit the
*.ipynb files to Autograder for grading your implementations in the middle or after implementing everything. You can partially grade some of the files in the middle, but please make sure that this also reduces the daily submission quota. Please check our Autograder tutorial for details.
5. Download .zip file
Once you have completed a notebook, download the completed
uniqueid_umid_A3.zip file, which is generated from your last cell of the
convolutional_networks.ipynb file. Before executing the last cell in
convolutional_networks.ipynb, please manually save your results so that the zip file includes all updates.
Make sure your downloaded zip file includes your most up-to-date edits; the zip file should include fully_connected_networks.ipynb, convolutional_networks.ipynb, fully_connected_networks.py, convolutional_networks.py , best_overfit_five_layer_net.pth, best_two_layer_net.pth, one_minute_deepconvnet.pth, overfit_deepconvnet.pth for this assignment.
6. Submit your python and ipython notebook files to Autograder
When you are done, please upload your work to Autograder (UMich enrolled students only). Your
*.ipynb files SHOULD include all the outputs. Please check your outputs up to date before submitting yours to Autograder.