def main()

in conv_split_cifar.py [0:0]
78 lines of code
21 McCabe index (conditional complexity)

def main():
    """
    Create the model and start the training
    """

    # Get the CL arguments
    args = get_arguments()

    # Check if the network architecture is valid
    if args.arch not in VALID_ARCHS:
        raise ValueError("Network architecture %s is not supported!"%(args.arch))

    # Check if the method to compute importance is valid
    if args.imp_method not in MODELS:
        raise ValueError("Importance measure %s is undefined!"%(args.imp_method))
    
    # Check if the optimizer is valid
    if args.optim not in VALID_OPTIMS:
        raise ValueError("Optimizer %s is undefined!"%(args.optim))

    # Create log directories to store the results
    if not os.path.exists(args.log_dir):
        print('Log directory %s created!'%(args.log_dir))
        os.makedirs(args.log_dir)

    # Generate the experiment key and store the meta data in a file
    exper_meta_data = {'ARCH': args.arch,
            'DATASET': 'SPLIT_CIFAR',
            'NUM_RUNS': args.num_runs,
            'TRAIN_SINGLE_EPOCH': args.train_single_epoch, 
            'IMP_METHOD': args.imp_method, 
            'SYNAP_STGTH': args.synap_stgth,
            'FISHER_EMA_DECAY': args.fisher_ema_decay,
            'FISHER_UPDATE_AFTER': args.fisher_update_after,
            'OPTIM': args.optim, 
            'LR': args.learning_rate, 
            'BATCH_SIZE': args.batch_size, 
            'MEM_SIZE': args.mem_size}
    experiment_id = "SPLIT_CIFAR_HERDING_%s_%r_%s_%s_%s_%s_%s-"%(args.arch, args.train_single_epoch, args.imp_method, 
            str(args.synap_stgth).replace('.', '_'), str(args.learning_rate).replace('.', '_'), 
            str(args.batch_size), str(args.mem_size)) + datetime.datetime.now().strftime("%y-%m-%d-%H-%M")
    snapshot_experiment_meta_data(args.log_dir, experiment_id, exper_meta_data)

    # Get the task labels from the total number of tasks and full label space
    if args.online_cross_val:
        num_tasks = K_FOR_CROSS_VAL
    else:
        num_tasks = NUM_TASKS - K_FOR_CROSS_VAL

    # Load the split cifar dataset
    data_labs = [np.arange(TOTAL_CLASSES)]
    datasets = construct_split_cifar(data_labs)

    # Variables to store the accuracies and standard deviations of the experiment
    acc_mean = dict()
    acc_std = dict()

    # Reset the default graph
    tf.reset_default_graph()
    graph  = tf.Graph()
    with graph.as_default():

        # Set the random seed
        tf.set_random_seed(args.random_seed)

        # Define Input and Output of the model
        x = tf.placeholder(tf.float32, shape=[None, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS])
        if args.imp_method == 'PNN':
            y_ = []
            for i in range(num_tasks):
                y_.append(tf.placeholder(tf.float32, shape=[None, TOTAL_CLASSES]))
        else:
            y_ = tf.placeholder(tf.float32, shape=[None, TOTAL_CLASSES])

        # Define the optimizer
        if args.optim == 'ADAM':
            opt = tf.train.AdamOptimizer(learning_rate=args.learning_rate)

        elif args.optim == 'SGD':
            opt = tf.train.GradientDescentOptimizer(learning_rate=args.learning_rate)

        elif args.optim == 'MOMENTUM':
            base_lr = tf.constant(args.learning_rate)
            learning_rate = tf.scalar_mul(base_lr, tf.pow((1 - train_step / training_iters), OPT_POWER))
            opt = tf.train.MomentumOptimizer(args.learning_rate, OPT_MOMENTUM)

        # Create the Model/ contruct the graph
        model = Model(x, y_, num_tasks, opt, args.imp_method, args.synap_stgth, args.fisher_update_after, 
                args.fisher_ema_decay, network_arch=args.arch)

        # Set up tf session and initialize variables.
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True

        time_start = time.time()
        with tf.Session(config=config, graph=graph) as sess:
            runs, task_labels_dataset = train_task_sequence(model, sess, datasets, args)
            # Close the session
            sess.close()
        time_end = time.time()
        time_spent = time_end - time_start

    # Store all the results in one dictionary to process later
    exper_acc = dict(mean=runs)
    exper_labels = dict(labels=task_labels_dataset)

    # If cross-validation flag is enabled, store the stuff in a text file
    if args.cross_validate_mode:
        acc_mean, acc_std = average_acc_stats_across_runs(runs, model.imp_method)
        fgt_mean, fgt_std = average_fgt_stats_across_runs(runs, model.imp_method)
        cross_validate_dump_file = args.log_dir + '/' + 'SPLIT_CIFAR_%s_%s'%(args.imp_method, args.optim) + '.txt'
        with open(cross_validate_dump_file, 'a') as f:
            if MULTI_TASK:
                f.write('HERDING: {} \t ARCH: {} \t LR:{} \t LAMBDA: {} \t ACC: {}\n'.format(args.arch, args.learning_rate, args.synap_stgth, acc_mean[-1,:].mean()))
            else:
                f.write('ARCH: {} \t LR:{} \t LAMBDA: {} \t ACC: {} \t Fgt: {} \t Time: {}\n'.format(args.arch, args.learning_rate, 
                    args.synap_stgth, acc_mean, fgt_mean, str(time_spent)))

    # Store the experiment output to a file
    snapshot_experiment_eval(args.log_dir, experiment_id, exper_acc)
    snapshot_task_labels(args.log_dir, experiment_id, exper_labels)