Introduction to Machine Learning Nanodegree

Deep Learning

Create your own Image Classifier

Overview

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.

Install

This project requires Python 2.7 and the following Python libraries installed:

• PyTorch
• ArgParse
• Jason
• PIL
• NumPy
• Pandas
• matplotlib
• scikit-learn You will also need to have software installed to run and execute an iPython Notebook We recommend students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Run

In a terminal or command window, navigate to the top-level project directory / (that contains this README) and run one of the following commands:

ipython notebook Image Classifier Project.ipynb or

jupyter notebook Image Classifier Project.ipynb This will open the iPython Notebook software and project file in your browser.

Or for Command Line In a terminal or command window, navigate to the top-level project directory / (that contains this README) and run one of the following commands:

• 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 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