Create Image Classifier with Terminal Commands!

1. Train

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"(vgg13, vgg16, or vgg19)
    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

2. Predict

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