def main():
args = parse_args()
model = load_checkpoint(args.checkpoint)
cat_to_name = load_cat_name = load_cat_names(args.category_names)
img_path = args.filepath
probs, classes = predict(img_path, model, int(args.top_k),gpu)
lables = [cat_to_name[str(index)] for index in classes]
probability = probs
print ('File selected: ' + img_path)
print (labels)
print (probability)
i=0
while i < len(labels):
print ("{} with a probability of {}".format(labels[i],probability[i]))
i = i + 1
def main():
args = parse_args()
gpu = args.gpu
model = load_checkpoint(args.checkpoint)
cat_to_name = load_cat_names(args.category_names)
img_path = args.filepath
probs, classes = predict(img_path, model, int(args.top_k), gpu)
labels = [cat_to_name[str(index)] for index in classes]
probability = probs
print('File selected: ' + img_path)
print(labels)
print(probability)
i = 0 # this prints out top k classes and probs as according to user
while i < len(labels):
print("{} with a probability of {}".format(labels[i], probability[i]))
i += 1 # cycle through
def main():
args = parse_args()
gpu = args.gpu
model = load_checkpoint(args.checkpoint)
cat_to_name = load_cat_names(args.category_names)
if args.filepath == None:
img_num = random.randint(1, 102)
image = random.choice(
os.listdir('./flowers/test/' + str(img_num) + '/'))
image_path = './flowers/test/' + str(img_num) + '/' + image
prob, classes = predict(image_path, model, int(args.top_k), gpu)
print('Image selected: ' + str(cat_to_name[str(img_num)]))
else:
image_path = args.filepath
prob, classes = predict(image_path, model, int(args.top_k), gpu)
print('File selected: ' + image_path)
print(prob)
print(classes)
print([cat_to_name[x] for x in classes])
def main():
args = parse_args()
gpu = args.gpu
model = load_checkpoint(args.checkpoint)
cat_to_name = load_cat_names(args.category_names)
if args.filepath:
img_path = args.filepath
else:
print('Cannot run prediction ..')
img_path = input("Please provide path to image: ")
probs, classes = predict(img_path, model, int(args.top_k), gpu)
print('\n======')
print('The filepath of the selected image is: ' + img_path, '\n')
print('The top K CLASSES for the selected image are: \n', classes, '\n')
print('The top K PROBABILITIES for the selected image are: \n ', probs,
'\n')
print('The top K CATEGORY NAMES for the selected image are: \n',
[cat_to_name[x].title() for x in classes])
print('======\n')
def main():
in_arg = get_input_args()
gpu = in_arg.gpu
model = load_checkpoint(in_arg.checkpoint)
cat_to_name = load_cat_names(in_arg.category_names)
if in_arg.filepath == None:
image_num = random.randint(1, 102)
image = random.choice(os.listdir('./flowers/test/' + str(image_num) + '/'))
img_path = './flowers/test/' + str(image_num) + '/' + image
prob, classes = predict(img_path, model, in_arg.top_k, gpu)
print("Selected Image is: " + str(cat_to_name[str(image_num)]))
else:
# Show random predicted image of the original image (displayed above) from a particular subfolder
#image = random.choice(os.listdir('./flowers/test/' + str(image_num) + '/'))
img_path = in_arg.filepath
prob, classes = predict(img_path, model, in_arg.top_k, gpu)
print("Selected Image is: " + img_path)
print("\nProbabilities are: \n", prob)
print("\nClasses are: \n", classes)
print("\nFlowers names are: \n", [cat_to_name[i] for i in classes])
img_torch = img_torch.float()
if gpu == 'gpu':
with torch.no_grad():
output = model.forward(img_torch.cuda())
else:
with torch.no grad():
output = model.forward(img_torch)
probability = F.softmax(output.data,dim=1)
probos = np.array(probability.topk(topk)[0][0])
index-to_class = {val: key for key, val in model.class_to_idx()}
top_classes = [np.int(index_to_class[each] for np.array(probability.topk(topk)[1][0])]
return probs, top_classes
def main():
args = parse_arg()
model = load.checkpoint(args.checkpoint)
cat_to_name = load_cat_name = load_cat_names(args.category_names)
img_path = args.filepath
probs, classes = predict(img_path, model, int(args.top_k),gpu)
lables = [cat_to_name[str(index)] for index in classes]
probability = probs
print ('File selected: ' + img_path)
print (labels)
print (probability)
i=0
while i < len(labels):
print ("{} with a probability of {}.format(labels[i], probability[i]]))
i = i + 1
if __name__ == "__main__":
main()