CSCI-566 Assignment 1

The objectives of this assignment

Implement the forward and backward passes as well as the neural network training procedure
Implement the widely-used optimizers and training tricks including dropout, weight decay, and L1&L2 regularizations
Implement convolutional neural networks from scratch
Use convolutional neural networks for image classification

Work on the assignment

Working on the assignment in an Anaconda environment is highly encouraged. In this assignment, please use Python 3.9. You will need to make sure that your conda setup is of the correct version of python.

Please refer to here for instructions on installing Anaconda.

Please see below for instructions on how to create and activate a conda environment on your terminal.

cd csci566-assignment1

conda init                      # If your conda isn't active already
conda create -n hw1 python=3.9 # If you didn't install it

# replace your_virtual_env with the virtual env name you want
conda activate hw1

# install dependencies
pip3 install numpy jupyter ipykernel scipy matplotlib tqdm

Work with IPython Notebook

To start working on the assignment, simply run the following command to start an ipython kernel.

conda activate hw1

# port is only needed if you want to work on more than one notebooks
jupyter notebook --port=your_port_number

You can open Jupyter on your IDE of choice, or your browser at the address localhost:<your_post_number> then work on each problem with their corresponding .ipynb notebooks. Check the python environment you are using on the top right corner. If the name of environment doesn't match, change it to your virtual environment in "Kernel>Change kernel".

Problems

In each of the notebook files, we indicate TODO or Your Code for you to fill in with your implementation. Majority of implementations will also be required under lib with specified tags.

Problem 1: Basics of Neural Networks (40 points)

The IPython Notebook Problem_1.ipynb will walk you through implementing the basics of neural networks.

Problem 2: CNNs for Image Classification (60 points)

The IPython Notebook Problem_2.ipynb will walk you through implementing a convolutional neural network (CNN) from scratch and using it for image classification.

How to submit

Run the following command to zip all the necessary files for submitting your assignment. Note that, in addition to your notebook with all cell outputs you will need to manually create a submission PDF for each problem set that compiles all generated plots / answers. Detailed instructions are at the end of the notebooks. The command below aggregates all notebooks and solution PDFs.

sh collectSubmission.sh

This will create a file named assignment1.zip, please rename it with your usc student id (eg. 4916525888.zip), and submit this file through the Google form. Do NOT create your own .zip file, you might accidentally include non-necessary materials for grading. We will deduct points if you don't follow the above submission guideline.

!! SUBMIT YOUR NOTEBOOK INCLUDING ALL CELL OUTPUTS, WE WILL NOT RERUN YOUR NOTEBOOKS !!

Questions?

If you have any question or find a bug in this assignment (or even any suggestions), we are more than welcome to assist through Piazza.

Again, NO INDIVIDUAL EMAILS WILL BE RESPONDED.

PLEASE USE PIAZZA TO POST QUESTIONS (under folder assignment1).

FAQ

Cannot get 30% accuracy for TinyNet in Problem 1
You can try to vary the batch size, epochs and learning rate decay. Please don't modify any code outside the TODO block.
What is a good starting learning rate?
There is a good article: https://medium.com/octavian-ai/which-optimizer-and-learning-rate-should-i-use-for-deep-learning-5acb418f9b2
When parsing the text data file for Problem 2 I get a charmap codec can't decode error.
This might be a platform dependent issue. In past years adding encoding="utf8" to the file open command helped in these cases.
What is the meta variable used for in Problem 2?
This variable is used to pass all values to the backward pass that are necessary to compute the gradients. You can use it as a dictionary to pass any desired values over to the backward function.
I experimented with the hyperparameters and tried many different combinations, which ones should I report?
The usual rule of thumb is to report results with the best hyperparameters you found. \
Am I allowed to change code outside the TODO blocks?
Unless specified otherwise (eg for hyperparameters) please do not change any code outside TODO blocks.
My %reload_ext autoreload command does not work, how to fix it?
This has been observed in the past and, whenever it was a problem, could be fixed by downgrading IPython to version 7.5.0: pip3 install ipython==7.5.
The zip file to submit is too large
Make sure you do not include your virtual environment, checkpoints, or datasets.
The submission script is not working on Windows machines
Try installing the zip package through cygwin.
How can I update my submission before the deadline?
You can submit your solution up to 10 times before the deadline without penalty. You do not need to remove any submitted files but can simply make another submission, we will only grade your latest submission. If you make a submission after the deadline we will still grade your latest submission, but deduct late days accordingly.
General debugging tips

Make sure your implementations matches the specified model layers perfectly.
Put print statements at various places inside your implementation code to make sure every module is working as it should. (but please remove any additional print statements for submission)

thiloshon / csci566-assignment1