'Returns the mean loss on the given test data and labels.
# Arguments
x: array-like, shape `(n_samples, n_features)`
Test samples where n_samples in the number of samples
and n_features is the number of features.
y: array-like, shape `(n_samples,)`
True labels for X.
**kwargs: dictionary arguments
Legal arguments are the arguments of `Sequential.evaluate`.
# Returns
score: float
Mean accuracy of predictions on X wrt. y.'

def score(self, x, y, **kwargs):

    kwargs = self.filter_sk_params(Sequential.evaluate, kwargs)
    loss = self.model.evaluate(x, y, **kwargs)
    if isinstance(loss, list):
        return loss[0]
    return loss
