# helper methods
def predict(network, image_path, class_names, topk=5):
''' Predict the class (or classes) of an image using a trained deep learning model.
Args:
network (nn_model.Neural_Network): The Neural_Network instance to use for the prediction.
image_path (str): The path to the image we want to test
class_names (dict of ints): The label map with the class names
topk (int): The number of top probabilities and classes we want.
Raises:
TODO: Add exceptions
Returns:
data_dict (dict): Dictionary containing 'predicted_indexes': indexes predicted by network,
'idx_to_class': mapped idx_to_class,
'classes': class names,
'probabilites': the probabilities for classes.
'''
# convert image
img = process_image(image_path, 224, 224)
# need to pass the image tensor with first argument of n where n represents our batch size
img.unsqueeze_(0)
# move to device
img.to(network.device)
# generate the prediction
network.model.to(network.device)
# enable eval mode, turn off dropout
network.model.eval()
# turn off the gradients since we are not updating params
with torch.no_grad():
img = img.to(network.device, dtype=torch.float)
# get the log softmax
output = network.model(img)
# get the prob
probabilities = torch.exp(output)
# get the top k values
top_probabilities, top_classes = probabilities.topk(topk, dim=1)
# remove the tensor cuda by moving to cpu, squeeze to remove dimensions and send to list to index
top_probabilities = top_probabilities.cpu().squeeze().tolist()
top_classes = top_classes.cpu().squeeze().tolist()
# generate the idx_to_class mapping dict
data_dict = map_idx_to_classes(network.model.classifier.class_to_idx, class_names, top_classes, top_probabilities)
return data_dict
