def predict(image_path, model, topk=5, gpu='cuda'):
''' Predict the class (or classes) of an image using a trained deep learning model.
'''
model.to(gpu)
image = processing_functions.process_image(image_path)
# Convert image to PyTorch tensor first
image = torch.from_numpy(image).type(torch.cuda.FloatTensor)
# Returns a new tensor with a dimension of size one inserted at the specified position.
image = image.unsqueeze(0)
output = model.forward(image)
probabilities = torch.exp(output)
# Probabilities and the indices of those probabilities corresponding to the classes
top_probabilities, top_indices = probabilities.topk(topk)
# Convert to lists
top_probabilities = top_probabilities.detach().type(torch.FloatTensor).numpy().tolist()[0]
top_indices = top_indices.detach().type(torch.FloatTensor).numpy().tolist()[0]
# Convert topk_indices to the actual class labels using class_to_idx
# Invert the dictionary so you get a mapping from index to class.
idx_to_class = {value: key for key, value in model.class_to_idx.items()}
#print(idx_to_class)
top_classes = [idx_to_class[index] for index in top_indices]
return top_probabilities, top_classes
help='class_to_name json file')
parser.add_argument('--gpu', type=str, default='cuda', help='GPU or CPU')
arguments = parser.parse_args()
# Load in a mapping from category label to category name
class_to_name_dict = processing_functions.load_json(arguments.json)
# Load pretrained network
model = model_functions.load_checkpoint(arguments.checkpoint)
print(model)
checkpoint = torch.load(arguments.checkpoint)
# Scales, crops, and normalizes a PIL image for the PyTorch model; returns a Numpy array
image = processing_functions.process_image(arguments.image_dir,
checkpoint['hidden_layer_units'])
# Display image
processing_functions.imshow(image)
# Highest k probabilities and the indices of those probabilities corresponding to the classes (converted to the actual class labels)
probabilities, classes = model_functions.predict(arguments.image_dir, model,
arguments.topk, arguments.gpu)
print(probabilities)
print(classes)
# Display the image along with the top 5 classes
processing_functions.display_image(arguments.image_dir, class_to_name_dict,
classes)