def evaluate(self, x, y, batch_size=32, verbose=1,
                 sample_weight=None, **kwargs):
        if self.model is None:
            raise Exception('The model needs to be compiled before being used.')
        if 'show_accuracy' in kwargs:
            kwargs.pop('show_accuracy')
            warnings.warn('The "show_accuracy" argument is deprecated, '
                          'instead you should pass the "accuracy" metric to '
                          'the model at compile time:\n'
                          '`model.compile(optimizer, loss, '
                          'metrics=["accuracy"])`')
        if kwargs:
            raise Exception('Received unknown keyword arguments: ' +
                            str(kwargs))
        return self.model.evaluate(x, y,
                                   batch_size=batch_size,
                                   verbose=verbose,
                                   sample_weight=sample_weight)
Computes the loss on some input data, batch by batch.
dataset/ETHPy150Open fchollet/keras/keras/models.py/Sequential.evaluate