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Assignment 3 Fully-Connected Neural Networks and Convolutional Neural

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Assignment 3
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.
Steps
1. Download the zipped assignment file
Click here to download the starter code
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 *.ipynb and *.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
EECS 498-007 / 598-005
Deep Learning for Computer Vision
Fall 2020
4. Evaluate your implementation on Autograder
Once you want to evaluate your implementation, please submit the *.py and *.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.
Justin Johnson
[email protected]
Website for UMich EECS course
EECS 498-007 / 598-005: Deep Learning for Computer Vision

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