'Returns the mean accuracy 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,)` or `(n_samples, n_outputs)`
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.
# Raises
ValueError: If the underlying model isn\'t configured to
compute accuracy. You should pass `metrics=["accuracy"]` to
the `.compile()` method of the model.'

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

    y = np.searchsorted(self.classes_, y)
    kwargs = self.filter_sk_params(Sequential.evaluate, kwargs)
    loss_name = self.model.loss
    if hasattr(loss_name, '__name__'):
        loss_name = loss_name.__name__
    if ((loss_name == 'categorical_crossentropy') and (len(y.shape) != 2)):
        y = to_categorical(y)
    outputs = self.model.evaluate(x, y, **kwargs)
    if (not isinstance(outputs, list)):
        outputs = [outputs]
    for (name, output) in zip(self.model.metrics_names, outputs):
        if (name == 'acc'):
            return output
    raise ValueError('The    model    is    not    configured    to    compute    accuracy.    You    should    pass    `metrics=["accuracy"]`    to    the    `model.compile()`    method.')
