How to use the braindecode.models.deep4.Deep4Net function in braindecode

)

    set_random_seeds(seed=20190706, cuda=cuda)

    n_classes = 4
    n_chans = int(train_set.X.shape[1])
    input_time_length = train_set.X.shape[2]
    if model == "shallow":
        model = ShallowFBCSPNet(
            n_chans,
            n_classes,
            input_time_length=input_time_length,
            final_conv_length="auto",
        ).create_network()
    elif model == "deep":
        model = Deep4Net(
            n_chans,
            n_classes,
            input_time_length=input_time_length,
            final_conv_length="auto",
        ).create_network()
    if cuda:
        model.cuda()
    log.info("Model: \n{:s}".format(str(model)))

    optimizer = optim.Adam(model.parameters())

    iterator = BalancedBatchSizeIterator(batch_size=batch_size)

    stop_criterion = Or(
        [
            MaxEpochs(max_epochs),

train_set = create_signal_target_from_raw_mne(train_cnt, marker_def, ival)
    test_set = create_signal_target_from_raw_mne(test_cnt, marker_def, ival)

    train_set, valid_set = split_into_two_sets(
        train_set, first_set_fraction=1-valid_set_fraction)

    set_random_seeds(seed=20190706, cuda=cuda)

    n_classes = 4
    n_chans = int(train_set.X.shape[1])
    if model == 'shallow':
        model = ShallowFBCSPNet(n_chans, n_classes, input_time_length=input_time_length,
                            final_conv_length=30).create_network()
    elif model == 'deep':
        model = Deep4Net(n_chans, n_classes, input_time_length=input_time_length,
                            final_conv_length=2).create_network()


    to_dense_prediction_model(model)
    if cuda:
        model.cuda()

    log.info("Model: \n{:s}".format(str(model)))
    dummy_input = np_to_var(train_set.X[:1, :, :, None])
    if cuda:
        dummy_input = dummy_input.cuda()
    out = model(dummy_input)

    n_preds_per_input = out.cpu().data.numpy().shape[2]

    optimizer = optim.Adam(model.parameters())

def __init__(self, in_chans, n_classes, input_time_length):
        super(HybridNetModule, self).__init__()
        deep_model = Deep4Net(
            in_chans,
            n_classes,
            n_filters_time=20,
            n_filters_spat=30,
            n_filters_2=40,
            n_filters_3=50,
            n_filters_4=60,
            input_time_length=input_time_length,
            final_conv_length=2,
        ).create_network()
        shallow_model = ShallowFBCSPNet(
            in_chans,
            n_classes,
            input_time_length=input_time_length,
            n_filters_time=30,
            n_filters_spat=40,