project_rubric.md

Udacity Project : Create Your Own Image Classifier

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Project Overview

Files Submitted:

For a successful project submission, you'll need to include these files in a ZIP archive (Not sure if we need to submit zip archive if submitted using GitHub):

• The completed Jupyter Notebook from Part 1 as an HTML file and any extra files you created that are necessary to run the code in the notebook

• The train.py and predict.py files from Part 2, as well as any other files necessary to run those scripts

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Project Rubric

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	Criteria	Meets Specifications
	Submission Files	The submission includes all required files. (Model checkpoints not required.)

Part 1 - Development Notebook

	Criteria	Meets Specifications
	Package Imports	All the necessary packages and modules are imported in the first cell of the notebook
	Training data augmentation	torchvision transforms are used to augment the training data with random scaling, rotations, mirroring, and/or cropping
	Data normalization	The training, validation, and testing data is appropriately cropped and normalized
	Data loading	The data for each set (train, validation, test) is loaded with torchvision's ImageFolder
	Data batching	The data for each set is loaded with torchvision's DataLoader
	Pretrained Network	A pretrained network such as VGG16 is loaded from torchvision.models and the parameters are frozen
	Feedforward Classifier	A new feedforward network is defined for use as a classifier using the features as input
	Training the network	The parameters of the feedforward classifier are appropriately trained, while the parameters of the feature network are left static
	Validation Loss and Accuracy	During training, the validation loss and accuracy are displayed
	Testing Accuracy	The network's accuracy is measured on the test data
	Saving the model	The trained model is saved as a checkpoint along with associated hyperparameters and the class_to_idx dictionary
	Loading checkpoints	There is a function that successfully loads a checkpoint and rebuilds the model
	Image Processing	The process_image function successfully converts a PIL image into an object that can be used as input to a trained model
	Class Prediction	The predict function successfully takes the path to an image and a checkpoint, then returns the top K most probably classes for that image
	Sanity Checking with matplotlib	A matplotlib figure is created displaying an image and its associated top 5 most probable classes with actual flower names

Part 2 - Command Line Application

	Criteria	Meets Specifications
	Training a network	train.py successfully trains a new network on a dataset of images and saves the model to a checkpoint
	Training validation log	The training loss, validation loss, and validation accuracy are printed out as a network trains
	Model architecture	The training script allows users to choose from at least two different architectures available from torchvision.models
	Model hyperparameters	The training script allows users to set hyperparameters for learning rate, number of hidden units, and training epochs
	Training with GPU	The training script allows users to choose training the model on a GPU
	Predicting classes	The predict.py script successfully reads in an image and a checkpoint then prints the most likely image class and it's associated probability
	Top K classes	The predict.py script allows users to print out the top K classes along with associated probabilities
	Displaying class names	The predict.py script allows users to load a JSON file that maps the class values to other category names
	Predicting with GPU	The predict.py script allows users to use the GPU to calculate the predictions