コード例 #1
0
ファイル: demo_cnn.py プロジェクト: lufeng22/nasbot
def main():
    """ Main function. """
    # Obtain the search space
    nn_domain = get_nn_domain_from_constraints('cnn', MAX_NUM_LAYERS,
                                               MIN_NUM_LAYERS, MAX_MASS,
                                               MIN_MASS, MAX_IN_DEGREE,
                                               MAX_OUT_DEGREE, MAX_NUM_EDGES,
                                               MAX_NUM_UNITS_PER_LAYER,
                                               MIN_NUM_UNITS_PER_LAYER)
    # Obtain a worker manager: A worker manager (defined in opt/worker_manager.py) is used
    # to manage (possibly) multiple workers. For a RealWorkerManager, the budget should be
    # given in wall clock seconds.
    worker_manager = RealWorkerManager(GPU_IDS, EXP_DIR)
    # Obtain a function caller: A function_caller is used to evaluate a function defined on
    # neural network architectures. We have defined the CNNFunctionCaller in
    # demos/cnn_function_caller.py. The train_params argument can be used to specify
    # additional training parameters such as the learning rate etc.
    train_params = Namespace(data_dir=DATA_DIR)
    func_caller = CNNFunctionCaller('cifar10',
                                    nn_domain,
                                    train_params,
                                    tmp_dir=TMP_DIR,
                                    reporter=REPORTER)

    # Run nasbot
    opt_val, opt_nn, _ = nasbot.nasbot(func_caller,
                                       worker_manager,
                                       BUDGET,
                                       reporter=REPORTER)

    # Print the optimal value and visualise the best network.
    REPORTER.writeln('\nOptimum value found: %f' % opt_val)
    visualise_file = os.path.join(EXP_DIR, 'cnn_opt_network')
    REPORTER.writeln('Optimal network visualised in %s.eps' % (visualise_file))
    visualise_nn(opt_nn, EXP_DIR + visualise_file)
コード例 #2
0
def main():
    """ Main function. """
    # Obtain the search space
    nn_domain = get_nn_domain_from_constraints('mlp-reg', MAX_NUM_LAYERS,
                                               MIN_NUM_LAYERS, MAX_MASS,
                                               MIN_MASS, MAX_IN_DEGREE,
                                               MAX_OUT_DEGREE, MAX_NUM_EDGES,
                                               MAX_NUM_UNITS_PER_LAYER,
                                               MIN_NUM_UNITS_PER_LAYER)
    # Obtain a worker manager: A worker manager (defined in opt/worker_manager.py) is used
    # to manage (possibly) multiple workers. For a RealWorkerManager, the budget should be
    # given in wall clock seconds.
    worker_manager = RealWorkerManager(GPU_IDS)
    # Obtain a function caller: A function_caller is used to evaluate a function defined on
    # neural network architectures. We have defined the MLPFunctionCaller in
    # demos/mlp_function_caller.py. The train_params can be used to specify additional
    # training parameters such as the learning rate etc.
    train_params = Namespace(data_train_file=get_train_file_name(DATASET))
    func_caller = MLPFunctionCaller(DATASET,
                                    nn_domain,
                                    train_params,
                                    reporter=REPORTER,
                                    tmp_dir=TMP_DIR)

    # Run nasbot
    opt_val, opt_nn, _ = nasbot.nasbot(func_caller,
                                       worker_manager,
                                       BUDGET,
                                       reporter=REPORTER)

    # Print the optimal value and visualise the best network.
    REPORTER.writeln('\nOptimum value found: ' % (opt_val))
    REPORTER.writeln('Optimal network visualised in mlp_opt_network.eps.')
    visualise_nn(opt_nn, 'mlp_opt_network')