def geomav(x, **kwargs):
    if len(x) == 0:
        return np.zeros(600)
    res = np.cumsum(utils.norm_geometric_average(utils.cdf_to_pdf(x)))
    return res

}

# Objective
l2_weight = 0.000
l2_weight_out = 0.000
def build_objective(interface_layers):
    # l2 regu on certain layers
    l2_penalty = nn.regularization.regularize_layer_params_weighted(
        interface_layers["regularizable"], nn.regularization.l2)
    # build objective
    return objectives.KaggleObjective(interface_layers["outputs"], penalty=l2_penalty)

# Testing
postprocess = postprocess.postprocess
test_time_augmentations = 100  # More augmentations since a we only use single slices
tta_average_method = lambda x: np.cumsum(utils.norm_geometric_average(utils.cdf_to_pdf(x)))


# nonlinearity putting a lower bound on it's output
def lb_softplus(lb):
    return lambda x: nn.nonlinearities.softplus(x) + lb


init = nn.init.Orthogonal()

rnn_layer = functools.partial(nn.layers.RecurrentLayer,
    W_in_to_hid=init,
    W_hid_to_hid=init,
    b=nn.init.Constant(0.1),
    nonlinearity=nn.nonlinearities.rectify,
    hid_init=nn.init.Constant(0.),