def train_batch(self, dataset, batch_size):

        if self.set_B_to_marginal_precision:
            assert not self.tied_B

            var = dataset.X.var(axis=0)

            self.B_driver.set_value( 1. / (var + .01))

        if self.stop_after_hack is not None:
            if self.monitor.examples_seen > self.stop_after_hack:
                logger.error('stopping due to too many examples seen')
                quit(-1)

        self.learn_mini_batch(dataset.get_batch_design(batch_size))
        return True
evaluates an apply of ImplValContainer objects entirely in local memory.
dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/models/s3c.py/S3C.train_batch--crossover with--dataset/ETHPy150Open ufora/ufora/ufora/FORA/python/Evaluator/LocalEvaluator.py/LocalEvaluator.evaluateDirectly