Create function for building best network

[craffel]

trafficstars

Would look something like this:

def construct_best_network(model_file, trial_glob):
    """
    Constructs the best-performing model according to the supplied
    hyperparameter optimization trials, and populates the parameters with the
    provided model file.

    Parameters
    ----------
    model_file : str
        Path to h5 file holding the parameters for this model.
    trial_glob : str
        Glob of h5 trial files.

    Returns
    -------
    layers : list of lasagne.layers.Layer
        Layers comprising the model.
    """
    # Load in all parameter optimization trials
    trial_files = glob.glob(trial_glob)
    trials = [deepdish.io.load(f) for f in trial_files]
    # Ignore trials with NaN objective
    trials = [t for t in trials if not np.isnan(t['best_objective'])]
    # Get the hyperparameters for the trial with the lowest objective value
    best_trial = sorted(trials, key=lambda t: t['best_objective'])[0]
    hyperparameters = best_trial['hyperparameters']
    # Load in the pre-trained parameters for the best performing models
    network_params = deepdish.io.load(model_file)
    # Create dictionary mapping network structure names to build functions
    build_network = {
        'pse_big_filter': build_pse_net_big_filter,
        'pse_small_filters': build_pse_net_small_filters,
        'dhs_big_filter': build_pse_net_big_filter,
        'dhs_small_filters': build_pse_net_small_filters}
    # PSE and DHS nets have different hyperparameters, so we must construct
    # kwargs based on what appears in hyperparameters
    kwargs = {
        # PSE trials do not have this hyperparameter by default, so as in
        # experiment_utils.run_trial, we must set the default value
        'downsample_frequency': hyperparameters.get('downsample_frequency', 1)}
    if 'n_attention' in hyperparameters:
        kwargs['n_attention'] = hyperparameters['n_attention']
    if 'n_conv' in hyperparameters:
        kwargs['n_conv'] = hyperparameters['n_conv']
    layers = build_network(
        (None, 1, None, feature_extraction.N_NOTES),
        # We will supply placeholders here but load in the values below
        np.zeros((1, feature_extraction.N_NOTES), theano.config.floatX),
        np.ones((1, feature_extraction.N_NOTES), theano.config.floatX),
        **kwargs)
    # Load in network parameter values
    lasagne.layers.set_all_param_values(
        layers[-1], network_params['X'])
    return layers

Would be used in precompute.py for a few of the experiments, and in match.py.