This is my solution to the AI Programming with Python Nanodegree Image Classifier project.

1. Create a image classifier using a pretrained model in dataset of flower images
2. Convert the classifier to a command line application

• Install Python 3.6.3
• Install Jupyter 5.7.0

1. Clone the project
2. Run jupyter notebook
3. Edit Image Classifier Project.ipynb

Note: I suggest to train this model using GPU

Train the network using train.py

python train.py data_directory

Set directory to save checkpoints

python train.py data_dir --save_dir save_directory

Choose architecture (alexnet, densenet121, vgg16)

python train.py data_dir --arch "vgg16"

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

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

Return top KK 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