Portfolio/Project 2 - Image Classifier Application

Deep Learning

Project: Image Classifier Project

Data

The data for this project is quite large - in fact, it is so large you cannot upload it onto Github. You will be training using 102 different types of flowers, where there ~20 images per flower to train on. Then you will use your trained classifier to see if you can predict the type for new images of the flowers (Quoted from Udacity).

Jupyter Notebook

This notebook implements the inception model in the jupyter notebook format. Most of the functions are static. To view the notebook, go to the following link

Project Notebook: Image Classifier

Application

The notebook is then converted into a command line application

Specifications

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.

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 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