Part 1 - Development Notebook All the necessary packages and modules are imported in the first cell of the notebook • torchvision transforms are used to augment the training data with random scaling, rotations, mirroring, and/or cropping • The training, validation, and testing data is appropriately cropped and normalized • The data for each set (train, validation, test) is loaded with torchvision's ImageFolder • The data for each set is loaded with torchvision's DataLoader • A pretrained network such as VGG16 is loaded from torchvision.models and the parameters are frozen • A new feedforward network is defined for use as a classifier using the features as input • The parameters of the feedforward classifier are appropriately trained, while the parameters of the feature network are left static • During training, the validation loss and accuracy are displayed • The network's accuracy is measured on the test data • There is a function that successfully loads a checkpoint and rebuilds the model • The trained model is saved as a checkpoint along with associated hyperparameters and the class_to_idx dictionary • The process_image function successfully converts a PIL image into an object that can be used as input to a trained model • The predict function successfully takes the path to an image and a checkpoint, then returns the top K most probably classes for that image • 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 train.py successfully trains a new network on a dataset of images and saves the model to a checkpoint • The training loss, validation loss, and validation accuracy are printed out as a network trains • The training script allows users to choose from at least two different architectures available from torchvision.models • The training script allows users to set hyperparameters for learning rate, number of hidden units, and training epochs • The training script allows users to choose training the model on a GPU • The predict.py script successfully reads in an image and a checkpoint then prints the most likely image class and it's associated probability • The predict.py script allows users to print out the top K classes along with associated probabilities • The predict.py script allows users to use the GPU to calculate the predictions • The predict.py script allows users to load a JSON file that maps the class values to other category names