Project code for Udacity's AI Programming with Python Nanodegree program. In this project, we first develop code for an image classifier built with PyTorch, then convert it into a command line application.

1. Load the data and define transforms for traning, valdation, and testing sets.

2. Buliding and training the classifier: creating the architecture with VGG model and my own classifier. We train the classifier with help from Udacity's GPU-enabled plstform.

3. Saving and loading the model to a checkpoint.pth.

4. Inference for classification: With the loading model, we randomly choose a image from test set and predict the category of it.

1. Image Classifier Project.ipynb.html: This is the Jupyter Notebook where I go through the whole flow from loading data to predicting flower category. The HTML form is convert from the ipynb, which is ease for mentor reviewing.

2. train.py: This is the first half code of the project, which contains loading data, buliding and training the classifier and saving the model.

3. predict.py: This is the last half code of the project, which contains loading the model and making predicting.

4. *_args.py: Define all the Argument Parser for the command line application.