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

Train a new network on a data set with train.py

Basic usage: python train.py data_directory

Prints out training loss, validation loss, and validation accuracy as the network trains

Options:

Set directory to save checkpoints: python train.py data_dir --save_dir save_directory

Choose architecture: python train.py data_dir --arch "vgg13"

Set hyperparameters: python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20

Use GPU for training: python train.py data_dir --gpu

Predict flower name from an image with predict.py along with the probability of that name. That is, you'll pass in a single image /path/to

/image and return the flower name and class probability.

Basic usage: python predict.py /path/to/image checkpoint Options:

Return top � K most likely classes: python predict.py input checkpoint --top_k 3

Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_to_name.json

Use GPU for inference: python predict.py input checkpoint --gpu