コード例 #1
0
def multi_driver_training(options):
    """ Train model using all drivers' data in the given datapath."""
    print("Loading data...")
    dataset = load_all(options)

    print("Building model and compiling functions...")
    net = models.cnn_1(options, output_size=len(dataset['label_map']))
    # net = models.softmax_only(output_size=len(dataset['label_map']))

    print("Starting training...")
    start_time = time.time()
    try:
        train_loop(dataset['train_data'], dataset['val_data'], net, options)
        print('Training Complete...')
    except KeyboardInterrupt:
        print('Keyboard Interrupt...')
    end_time = time.time()

    print('--------------------')
    print('   Saving model, check logs for results.\n'
          '   Time taken: {0}\n'
          .format(end_time - start_time))

    utils.save_model(model=get_all_param_values(net), options=options)
    return net
コード例 #2
0
def per_driver_train_predict(options):
    """ Predict output probabilities for each driver.
        If `options.train` is true, this trains a model"""
    # TODO: support for predict only
    # TODO: support for pretrained weights
    print("Loading data...")
    dataset = load_all(options)
    all_outputs = deque([])
    for driver_index in range(len(dataset['label_map'])):
        driver_id = dataset['label_map'][driver_index]
        print('Working on Driver {0}'.format(driver_id))
        net = _train_single(driver_index, dataset, options)
        utils.save_model(model=get_all_param_values(net), options=options, driver_id=driver_id)
        print('Training complete for driver {0}, making predictions...'.format(driver_id))
        outputs = _predict_single(net, driver_id, options)
        utils.save_outputs(outputs, options, driver_id=driver_id)
        all_outputs.extend(outputs)
    utils.save_outputs(all_outputs, options)
    return