Final project for Machine Learning and Statistical Analysis course

Final_ML contains the code of interface and the model training process.

models.py contains the AlexNet class

test.py contains methods:

• create_model() saves the model in json format for reusability
• test() trains the model and saves the final weights in model.h5
• give_label(X) predicts the label of a given input X by loading pretrained weights

data.py contains methods:

-prep_data() takes references from train.csv and uploaded images from folder train. Then it resizes it to 2242243 format and balances the first 6000 images.

kaggle.py contains methods:

• kaggle_test() takes references from sample_submission.csv (given by kaggle) and uploaded images from folder static
• generate_kaggle_csv() applied model prediction on each test sample and saves the data set with labeled predictions on predictions.csv

main.py contains methods:

• analyzeFrame(frame) call method give_label
• homePage() renders the page of web application,while requiesting Post method, calls analyzeFrame for a given input picture
• allowed_file() checks whether the uploaded file is in picture allowed formats