binary-search-in-neural-nets

Drexel AI's Fall Term research project on efficiently searching for accurate neural net architectures

How to run

With python version over 3.6(I'm using 3.7.1), do

pip install -r requirements.txt

Then run

python run.py

There are some arguments that can be used. For example, to change epoch size, you can do

python run.py --epoch=200

The list of arguments can be seen at binary_search_networksbinary_search_parser.py

Current features

• Gets data.
• Preprocesses data.
• Given input n, trains model. Prints out accuracy.
• Tests model. Prints out accuracy.

Check list

Deadline: December 12, 2020

• Pick a Dataset that can be generalized -> Using titanic dataset courtesy of https://www.openml.org/d/40945
• Determine trendline over two end points, determine the slope and determine side to get rid of
• Find the maximum value in a partially sorted array
• Adapt the code so that user can pass n as input and run the entire pipeline (train + test + save)
• Plot the loss and accuracy given a neural net for our problem
• Add another dataset (churn model is added).
• Implement linear search (O(N)) # need to train the model N times.
• Implement binary search (O(log(N))) # need to train the model N times.
• Determine trendline over two end points, determine the slope and determine side to get rid of.
• Integrate the searches to pipeline.
• Adapt the code so that user can pass n as input and run the entire pipeline (train + test + save).
• Plot the loss and accuracy given a neural net for our problem.