def main():
trainloader, v_loader, testloader = futils.load_data(root)
model, optimizer, criterion = futils.network(structure,dropout,hidden_layer1,lr,device)
futils.training(model, optimizer, criterion, epochs, 40, trainloader, device)
futils.save_checkpoint(model,path,structure,hidden_layer1,dropout,lr)
print("Done")
ap.add_argument('data_dir', nargs='*', action="store", default="./flowers/")
ap.add_argument('--gpu', dest="gpu", action="store", default="gpu")
ap.add_argument('--save_dir', dest="save_dir", action="store", default="./checkpoint.pth")
ap.add_argument('--learning_rate', dest="learning_rate", action="store", default=0.001)
ap.add_argument('--dropout', dest="dropout", action="store", default=0.5)
ap.add_argument('--epochs', dest="epochs", action="store", type=int, default=1)
ap.add_argument('--arch', dest="arch", action="store", default="vgg16", type=str)
ap.add_argument('--hidden_units', type=int, dest="hidden_units", action="store", default=120)
pa = ap.parse_args()
where = pa.data_dir
path = pa.save_dir
lr = pa.learning_rate
structure = pa.arch
dropout = pa.dropout
hidden_layer1 = pa.hidden_units
power = pa.gpu
epochs = pa.epochs
trainloader, v_loader, testloader = futils.load_data(where)
model, optimizer, criterion = futils.nn_setup(structure, dropout, hidden_layer1, lr, power)
futils.train_network(model, optimizer, criterion, epochs, 20, trainloader, power)
futils.save_checkpoint(path, structure, hidden_layer1, dropout, lr)
print("!!! Training Sequence Completed !!!")
type=str)
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
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:
action="store",
type=str,
default="vgg16")
ap.add_argument('--hidden_units',
dest="hidden_units",
action="store",
type=int,
default=120)
pa = ap.parse_args()
where = pa.data_dir
path = pa.save_dir
lr = pa.learning_rate
structure = pa.arch
dropout = pa.dropout
hidden_layer1 = pa.hidden_units
power = pa.gpu
epochs = pa.epochs
train_datasets, trainloader, valid_loader, testloader = futils.load_data(where)
model, criterion, optimizer = futils.nn_setup(structure, dropout,
hidden_layer1, lr, power)
futils.train_network(model, criterion, optimizer, epochs, 20, trainloader,
validloader, power)
futils.save_checkpoint(train_datasets, path, structure, hidden_layer1, dropout,
lr)
print("The Model is trained")
