Classifying Flower Images with Convolutional Neural Net

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.

The project development (part 1) is where I created and trained the network. The application part of the project is taking what I made and applying it to a command line, argument based, generalization that can be used to train and predict.

Click for NN Development

Part 2 - Building the command line application

Now that you've built and trained a deep neural network on the flower data set, it's time to convert it into an application that others can use. Your application should be a pair of Python scripts that run from the command line. For testing, you should use the checkpoint you saved in the first part.

Specifications

The project submission must include at least two files train.py and predict.py. The first file, train.py, will train a new network on a dataset and save the model as a checkpoint. The second file, predict.py, uses a trained network to predict the class for an input image. Feel free to create as many other files as you need. Our suggestion is to create a file just for functions and classes relating to the model and another one for utility functions like loading data and preprocessing images. Make sure to include all files necessary to run train.py and predict.py in your submission.

This is implemented as a generalization of part 1. User of this command line app can load in any torchvision network, and specify the desired hidden units and output.

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, 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