PyTorch Flower Image Classifier

*** Command Line Application ***

• Training a network: train.py successfully trains a new network on a dataset of images
• 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

*** Development Steps ***

1. Package Imports: All the necessary packages and modules are imported
2. Training data augmentation: torchvision transforms are used to augment the training data with random scaling, rotations, mirroring, and/or cropping
3. Data normalization: The training, validation, and testing data is appropriately cropped and normalized
4. Data loading: The data for each set (train, validation, test) is loaded with torchvision's ImageFolder
5. Data batching: The data for each set is loaded with torchvision's DataLoader
6. Pretrained Network: A pretrained network such as VGG16 is loaded from torchvision.models and the parameters are frozen
7. Feedforward Classifier: A new feedforward network is defined for use as a classifier using the features as input
8. Training the network: The parameters of the feedforward classifier are appropriately trained, while the parameters of the feature network are left static
9. Validation Loss and Accuracy: During training, the validation loss and accuracy are displayed
10. Testing Accuracy: The network's accuracy is measured on the test data
11. Saving the model: The trained model is saved as a checkpoint along with associated hyperparameters and the class_to_idx dictionary
12. Loading checkpoints: There is a function that successfully loads a checkpoint and rebuilds the model
13. Image Processing: The process_image function successfully converts a PIL image into an object that can be used as input to a trained model
14. 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
15. Sanity Checking with matplotlib: A matplotlib figure is created displaying an image and its associated top 5 most probable classes with actual flower names

If workspace_utils.py is producing errors, simply do not use that module. In the train_classifier method, remove 'with active_session():' and un-ident the code inside it.