In this assignment, we are going to see if we can teach a computer to distinguish living things from non-living things. More precisely, you will implement the perceptron algorithm to detect whether or not an image contains an animal or not.

Contents

• General Guidelines
• Problem Statement
• Dataset
• Perceptron Model
• Training and Development
• Extra Credit Suggestion
• Provided Code Skeleton

• For general instructions, see the main MP page and the course policies.

You should submit on Gradescope:

• A copy of perceptron.py containing all your new code

You are given a dataset consisting of images, that either contain pictures of animals or not. Your task is to write a perceptron algorithm to classify which images have animals in them. Using the training set, you will learn a perceptron classifier that will predict the right class label given an unseen image. Use the development set to test the accuracy of your learned model. We will have a separate (unseen) test set that we will use to run your code after you turn it in. You may use NumPy in this MP to program your solution. Aside from that library, no other outside non-standard libraries can be used.

Dataset

The data set can be downloaded here: data (gzip) or data (zip). When you uncompress this, you'll find a binary object that our reader code will unpack for you.

Perceptron Model

The perceptron model is a linear function that tries to separate data into two or more classes. It does this by learning a set of weight coefficients $w_i$ and then adding a bias $b$. Suppose you have features $x_1,\dots,x_n$ then this can be expressed in the following fashion: $$f_{w,b} (x) = \sum_{i=1}^n w_i x_i + b$$ You will use the perceptron learning algorithm to find good weight parameters $w_i$ and $b$ such that $\mathrm{sign}(f_{w,b}(x)) \geq 0$ when there is an animal in the image and $\mathrm{sign}(f_{w,b}(x)) < 0$ when there is a no animal in the image. Note that in our case, we have 3072 features because each image is 32x32 and they each have RGB color channels yielding 32*32*3 = 3072.

Training and Development

Use only the training set to learn the weights.

Extra Credit Suggestion

Perceptron is a simple linear model, and although this is sufficient in a lot of cases, it has its limits. Implement K-Nearest Neighbors. Try and see what's the highest accuracy you can get, and find some justification (just for fun no need to submit) for why your choice of model is superior to perceptron for this particular task. You must implement this algorithm on your own with only standard libraries and NumPy. The choice of K should be chosen based on experimentation.

Provided Code Skeleton

We have provided ( tar zip) all the code to get you started on your MP, which means you will only have to implement the logic behind perceptron.

• reader.py - This file is responsible for reading in the data set. It makes a giant NumPy array of feature vectors corresponding with each image.

• mp5.py - This is the main file that starts the program, and computes the accuracy, precision, recall, and F1-score using your implementation of perceptron.

• perceptron.py This is the file where you will be doing all of your work.

Inside the code ...

• The function classify() takes as input the training data, training labels, development set, learning rate, and maximum number of iterations.
• The training data provided is the output from reader.py.
• The training labels is the list of labels corresponding to each image in the training data.
• The development set is the NumPy array of images that you are going to test your implementation on.
• The learning rate is the hyperparameter you specified with --lrate (it is 1 by default). Reset the value inside your classify() function if you want something other than the default value for your final submission.
• The maximium number of iterations is the number you specified with --max_iter (it is 10 by default). Please do not reset this value inside your code.
• You will have classify() output the predicted labels for the development set from your perceptron model.
• The function classifyEC() is a function where you can implement the extra credit, if you decide to attempt it. If you use the --extra flag then classifyEC() will be ran instead of classify().

NOTE: In classify() only implement perceptron on the raw data. Do NOT do any extra credit or anything extra in classify(). Do not modify the provided code. You will only have to modify perceptron.py.

To understand more about how to run the MP, run python3 mp5.py -h in your terminal.