This repo holds the final project of my class, classical problem modeling.
For original final project paper, please click here.
In this repo, I implemented three training ways(kNN, SVM, CNN) for Google Quick Draw dataset.
I only use 5000 rows of data picked from 5 class of the original dataset. They are Apple, Banana, Blueberry, Pineapple, Strawberry.
5000 data were seperated for 4500 training data, and 500 test data. If you wanna redo my experiment, you may need to extract data by yourself since I don't provide the original data here.
problem_6/solution.m stands for problem 6 solution.
problem_7/solution.m stands for problem 7 solution.
problem_8/train_cnn.py stands for CNN training based on Keras implemented Python code.
problem_8/train_svm.py stands for SVM training based on sklearn implemented Python code.
problem_8/train_knn.m stands for kNN traning and data extracting based on Matlab implemented code.
Hawkins Zhao@2017