Here are the steps we follow
- Create data
- Visualize data
- Run Deep Learning on data
- Create predicted data
- Visualize predicted data
We are seeing an accuracy of 98.24% in predicting circle data.
$ python circle.py > circle_data.csv
$ python visualize.py circle_data.csv
$ python circle_dl.py train circle_dl.py circle_data.csv
$ python circle_dl.py predict circle_data.csv circle_predicted.csv
$ python visualize.py circle_predicted.csv
We are seeing an accuracy of 95.64% in predicting complicated data.
$ python complicated.py > complicated_data.csv
$ python visualize.py complicated_data.csv
$ python complicated_dl.py train complicated_data.csv
$ python complicated_dl.py predict complicated_data.csv complicated_predicted.csv
$ python visualize.py complicated_predicted.csv
We are seeing an accuracy of 98.08% in predicting complicated data.
$ python complicated1.py > complicated1_data.csv
$ python visualize.py complicated1_data.csv
$ python complicated1_dl.py train complicated1_data.csv
$ python complicated1_dl.py predict complicated1_data.csv complicated1_predicted.csv
$ python visualize.py complicated1_predicted.csv