This repository contains a series of CNNs trained on "imagized" data from the Mexican Covid-19 dataset.
Built Using:
- python 3.7.6
- pytorch 1.5.0+cpu
- torchvision 0.6.0+cpu
Modules:
- network_dictionary_builder.py:
builds a series of randomized CNNs based on provided test tensor.
kwargs can be used to apply constraints including a list of optimizers to test for all nets.
includes training, importing, and exporting functions for entire net dict.
NOT specific to CovidCNN. Could be utilized in other applications. - network_dictionary_analyzer.py:
aggregates data for all nets/optimizers in a given network dictionary.
provides trending functions to analyze and compare randomized nets.
NOT specific to CovidCNN. Could be utilized in other applications. - utilities.py:
functions required to support CovidCNN efforts.
includes class for initializing, storing, and recalling train/test data.
translates binary string data (e.g. "00100110") + pt age to image and vice versa.
See candidate_cnn_builder.ipynb for an example of how these modules can be implemented.