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