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")