Command line application to train & predict with a classifier for flowers species
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 KK most likely classes: python predict.py input checkpoint --top_3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_to_nameson Use GPU for inference: python predict.py input checkpoint --gpu