custom_layer_activation.py 文件源码

def search_model(experiment_label, steps, batch_size=32):
    """ This is where we put everythin together.
    We get the dataset, build the Training and Experiment objects, and run the experiment.
    The experiments logs are generated in ~/minos/experiment_label
    We use the CpuEnvironment to have the experiment run on the cpu, with 2 parralel processes.
    We could use GpuEnvironment to use GPUs, and specify which GPUs to use, and how many tasks
    per GPU
    """
    batch_iterator, test_batch_iterator, nb_classes = get_reuters_dataset(batch_size, max_words)
    layout = build_layout(max_words, nb_classes)
    training = Training(
        Objective('categorical_crossentropy'),
        Optimizer(optimizer='Adam'),
        Metric('categorical_accuracy'),
        epoch_stopping_condition(),
        batch_size)
    parameters = custom_experiment_parameters()
    experiment = Experiment(
        experiment_label,
        layout,
        training,
        batch_iterator,
        test_batch_iterator,
        CpuEnvironment(n_jobs=1),
        parameters=parameters)
    run_ga_search_experiment(
        experiment,
        population_size=100,
        generations=steps,
        resume=False,
        log_level='DEBUG')