def main():
model = futils.load_checkpoint(path)
with open('cat_to_name.json', 'r') as json_file:
cat_to_name = json.load(json_file)
probabilities = futils.predict(path_image, model, number_of_outputs,
device)
labels = [
cat_to_name[str(index + 1)] for index in np.array(probabilities[1][0])
]
probability = np.array(probabilities[0][0])
i = 0
while i < number_of_outputs:
print("{} with a probability of {}".format(labels[i], probability[i]))
i += 1
print("All done!")
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
ap.add_argument('--category_names',
dest="category_names",
action="store",
default='cat_to_name.json')
ap.add_argument('--gpu', default="gpu", action="store", dest="gpu")
pa = ap.parse_args()
path_image = pa.input_img
number_of_outputs = pa.top_k
power = pa.gpu
input_img = pa.input_img
path = pa.checkpoint
training_loader, testing_loader, validation_loader = futils.load_data()
futils.load_checkpoint(path)
with open('cat_to_name.json', 'r') as json_file:
cat_to_name = json.load(json_file)
probabilities = futils.predict(path_image, model, number_of_outputs, power)
labels = [
cat_to_name[str(index + 1)] for index in np.array(probabilities[1][0])
]
probability = np.array(probabilities[0][0])
i = 0
while i < number_of_outputs:
print("{} with a probability of {}".format(labels[i], probability[i]))
i += 1
action="store")
ap.add_argument('--top_k', default=5, dest="top_k", action="store", type=int)
ap.add_argument('--category_names',
dest="category_names",
action="store",
default='cat_to_name.json')
ap.add_argument('--gpu', default="gpu", action="store", dest="gpu")
pa = ap.parse_args()
path_image = pa.input_img
number_of_outputs = pa.top_k
power = pa.gpu
input_img = pa.input_img
checkpoint_path = pa.checkpoint
model = futils.load_checkpoint(checkpoint_path)
with open('cat_to_name.json', 'r') as json_file:
cat_to_name = json.load(json_file)
# Process image and predict label via model
img = futils.process_image(input_img)
probabilities = futils.predict(img, model, number_of_outputs, power)
# Display probabilities and labels for each output specified
labels = [
cat_to_name[str(index + 1)] for index in np.array(probabilities[1][0])
]
probability = np.array(probabilities[0][0])
print("\n\n**Results from image {} using pretrained model checkpoint {}**".
