Drexel AI's Fall Term research project on efficiently searching for accurate neural net architectures
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
- Gets data.
- Preprocesses data.
- Given input n, trains model. Prints out accuracy.
- Tests model. Prints out accuracy.
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.