def main():
    parser = argparse.ArgumentParser(description='Evaluate .')
    parser.add_argument('rundir', type=str, help='directory of tf checkpoint file')
    parser.add_argument('--model', type=str, help="Neural network architecture. 'lstm', 'rnn' or 'cnn' (default lstm)", default='lstm')
    parser.add_argument('--gpu', type=int, help='Select gpu (e.g. 0), via environment variable CUDA_VISIBLE_DEVICES (default None)', default=None)
    args = parser.parse_args()
    ' GPU management '
    allow_gpu_mem_growth = True
    gpu_memory_fraction = 1
    gpu_id = args.gpu
    if (args.gpu is not None):
        os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
        os.environ['CUDA_VISIBLE_DEVICES'] = gpu_id
    dataloader = Dataloader(datafolder='data/eval', batchsize=500)
    '\n    Load\n    parameters\n    from init_from model\n    '
    with open(os.path.join(args.rundir, 'args.pkl'), 'rb') as f:
        modelargs = pickle.load(f)
        '\n    Create\n    new\n    model\n    object\n    with same parameter '
    print('building model graph')
    if (args.model in ['rnn', 'lstm']):
        model = rnn_model.Model(n_input=modelargs['n_input'], n_classes=modelargs['n_classes'], n_layers=modelargs['n_layers'], batch_size=dataloader.batchsize, adam_lr=modelargs['adam_lr'], rnn_cell_type=args.model, dropout_keep_prob=modelargs['dropout_keep_prob'], n_cell_per_input=modelargs['n_cell_per_input'], gpu=0)
        evaluate = evaluate_rnn
    if (args.model == 'cnn'):
        model = cnn_model.Model(n_input=modelargs['n_input'], n_classes=modelargs['n_classes'], n_layers=modelargs['n_layers'], adam_lr=0.001, dropout_keep_prob=modelargs['dropout_keep_prob'], n_cell_per_input=modelargs['n_cell_per_input'], gpu=gpu_id)
        evaluate = evaluate_cnn
    (probabilities, targets, observations) = evaluate(model, dataloader, init_dir=args.rundir, print_every=20, gpu_memory_fraction=gpu_memory_fraction, allow_gpu_mem_growth=allow_gpu_mem_growth)
    np.save(os.path.join(args.rundir, 'eval_probabilities.npy'), probabilities)
    np.save(os.path.join(args.rundir, 'eval_targets.npy'), targets)
    np.save(os.path.join(args.rundir, 'eval_observations.npy'), observations)
def main():
    parser = argparse.ArgumentParser(description='Evaluate .')
    parser.add_argument('rundir', type=str, help='directory of tf checkpoint file')
    parser.add_argument('--model', type=str, help="Neural network architecture. 'lstm', 'rnn' or 'cnn' (default lstm)", default='lstm')
    parser.add_argument('--gpu', type=int, help='Select gpu (e.g. 0), via environment variable CUDA_VISIBLE_DEVICES (default None)', default=None)
    args = parser.parse_args()
    ' GPU management '
    allow_gpu_mem_growth = True
    gpu_memory_fraction = 1
    gpu_id = args.gpu
    if (args.gpu is not None):
        os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
        os.environ['CUDA_VISIBLE_DEVICES'] = gpu_id
    dataloader = Dataloader(datafolder='data/eval', batchsize=500)
    '\n    Load\n    parameters\n    from init_from model\n    '
    with open(os.path.join(args.rundir, 'args.pkl'), 'rb') as f:
        modelargs = pickle.load(f)
        '\n    Create\n    new\n    model\n    object\n    with same parameter '
    print('building model graph')
    if (args.model in ['rnn', 'lstm']):
        model = rnn_model.Model(n_input=modelargs['n_input'], n_classes=modelargs['n_classes'], n_layers=modelargs['n_layers'], batch_size=dataloader.batchsize, adam_lr=modelargs['adam_lr'], rnn_cell_type=args.model, dropout_keep_prob=modelargs['dropout_keep_prob'], n_cell_per_input=modelargs['n_cell_per_input'], gpu=0)
        evaluate = evaluate_rnn
    if (args.model == 'cnn'):
        model = cnn_model.Model(n_input=modelargs['n_input'], n_classes=modelargs['n_classes'], n_layers=modelargs['n_layers'], adam_lr=0.001, dropout_keep_prob=modelargs['dropout_keep_prob'], n_cell_per_input=modelargs['n_cell_per_input'], gpu=gpu_id)
        evaluate = evaluate_cnn
    (probabilities, targets, observations) = evaluate(model, dataloader, init_dir=args.rundir, print_every=20, gpu_memory_fraction=gpu_memory_fraction, allow_gpu_mem_growth=allow_gpu_mem_growth)
    np.save(os.path.join(args.rundir, 'eval_probabilities.npy'), probabilities)
    np.save(os.path.join(args.rundir, 'eval_targets.npy'), targets)
    np.save(os.path.join(args.rundir, 'eval_observations.npy'), observations)