Lab 6: Convolutional Neural Networks
Due November 3 by midnight

Starting point code

Use Teammaker to form your team. You can log in to that site to indicate your partner preference. Once you and your partner have specified each other, a GitHub repository will be created for your team.

Introduction

In this lab you will be using Keras and aitk to explore convolutional neural networks. You will start by experimenting with an already constructed convNet to learn the MNIST dataset. Once you understand how to use Keras and aitk to construct, train, and test a convNet, you will create your own convNet to classify fashion images of clothing.

Relative to all of the previous labs in this class, much of the design of the lab is left up to you. This is intended to start to prepare you for your upcoming AI project. You will likely be using some type of publicly available software, like Keras, to complete your project. You should get used to consulting the documentation when you have questions. I am also willing to help, of course.

Using Keras and aitk

Before using Keras and aitk, you need to activate a virtual environment that contains all of the software packages that you'll need:

source /usr/swat/bin/CS63env

deactivate

We will be using Jupyter Lab to enable lots of interactive experiments with the deep networks. Once you have activated the virtual environment, you should cd to the directory containing your repo for this lab and then you can start up jupyter like this:

jupyter lab

Classifying handwritten digits

Within jupyter, open up the file digits.ipynb and read through the notebook. Doing deep learning will typically require the steps listed below. Take your time and try to understand how each of these steps is being handled for learning the MNIST dataset.

1. Prepare the data
   This may include loading, reshaping, normalizing, and dividing the data into separate training and testing sets.
2. Construct the model
   We will be using Sequential() models. Add the layers in order from input to output.
3. Compile the model
   Specify the optimizer, loss, and metric that will be used.
4. Train the model
   Determine the number of epochs to complete.
5. Test the model
   After training is complete, analyze the results.

Typically the network's performance will not be as successful as you would like. To improve the performance you will need to iterate over these steps multiple times. Here are some ways to consider adjusting the deep learning network:

• If loss is still dropping, you may just need to train the model for more epochs.
• Tweak the model architecture, by adding more layers, or changing the parameters within existing layers, such as the number and size of filters.
• Try a different optimizer method.
• Consider adding dropout layers to improve generalization.

Once you feel that you understand what is happening with this network to learn to classify handwritten digits you can move on to classifying images of fashion apparel.

Classifying fashion

• 0: t-shirt
• 1: trouser
• 2: pullover
• 3: dress
• 4: coat
• 5: sandal
• 6: shirt
• 7: sneaker
• 8: bag
• 9: ankle boot

In the collection of images below, for each category, there are three rows of example images.

Writeup

In the file writeup.tex:

• Describe the neural network you built that showed the best validation performance on the test data. Use screen shots of both the network summary and the training graphs to support your explanation.
• Analyze how the network has used its convolution and/or pooling layers to find helpful features for classifying these images. Use screen shots of the feature maps to support your points.
• Report on which categories within the fashion data proved to be the easiest and hardest to learn. Speculate about why this was the case.

You can edit the writeup.tex file with any text editor. There's lots of great help available online for latex; just google "latex topic" to find lots of tutorials. To compile your latex document into a pdf, run the following command:

pdflatex writeup.tex

Submitting