1. Motivation
2. File description
3. How to interact with your project
4. Licensing
5. Authors
6. Acknowledgements

This project is part of Udacity Data Scientist Nanodegree.

In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application.

In this project, you'll train an image classifier to recognize different species of flowers.

In this first part of the project, We work through a Jupyter notebook to implement an image classifier with PyTorch.

Building the command line application that others can use. The application is a pair of Python scripts that run from the command line.

We'll be using this dataset of 102 flower categories, you can see a few examples below.

Image Classifier Project.ipynb - Jupyter notebook to implement an image sorter with PyTorch.

cat_to_name.jason - Is a mapping from category label to category name. This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers.

Train.py - train a new network on a dataset and save the model as a checkpoint.

Predict.py - uses a trained network to predict the class for an input image.

Util.py - The file with auxiliary functions for the operation of Train.py and Predict.py.

helper.py - Auxiliary file provided by Udacity

• Train a new network on a data set with train.py

• Train a new network on a data set with train.py

  • Basic Usage : python train.py data_directory
  • Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains
  • Options:
    • Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory
    • Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16"
    • Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20
    • Use GPU for training: python train.py data_dir --gpu 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

License file

Udacity provide some tips and guide me, but for the most part the code is from Ricardo Coelho.

Neural Networks

argparse module

Deep Learning With PyTorch