CS 5043: HW5: Complex Convolutional Neural Networks

Assignment notes:

Deadline: Thursday, April 8th @11:59pm.
Hand-in procedure: submit a pdf to Gradescope
This work is to be done on your own. While general discussion about Python, Keras and Tensorflow is encouraged, sharing solution-specific code is inappropriate. Likewise, downloading solution-specific code is not allowed.
Do not submit MSWord documents.

Problem

Our objective with this homework is to explore non-serial deep network architectures, constructed using the Keras Model API.

The data set and classification problem is the same as in HW 4.
Implement one more more non-serial deep networks. Possibilities include:
- An inception-style network.
- A network with more than one input image at the same time (e.g., two input images separated by 10 time steps).
- A network with K input images, each processed by the same single-image recognizer and the outputs combined using a recurrent neural network.

For networks with multiple input images, you will need to alter either the data loading process or the generator process. The key is to make sure that all of the images that are presented as part of one example all come from the same object. It is fine to share this code with the rest of the class if you have a nice solution.

Hand in a PDF file that contains:

Code for generating and training the network. Some useful UNIX command line programs:
- enscript: translate code (e.g., py files) into postscript files
- ps2pdf: translate postscript files into pdf files
- pdfunite: merge several pdf files together
The same learning curve and confusion matrix figures as produced in HW 4
A report of the mean multi-class AUC
Your batch files used to run the experiments for your shallow and deep networks
A statistical test of the mean validation set performance of your best model from HW4 and your model for this assignment

50 pts: Model generation code. Is it correct? clean? documented?
50 pts: Model figures and performance. To achieve full credit you must show a statistically significant improvement over your best HW4 model.
10 pts: You solved the bonus problem of achieving average of 0.9 on the validation data set your model across the five rotations, or you beat the best of the TA/instructor solutions

Last modified: Wed Mar 24 23:00:04 2021