(self, batch_size)
Get a set of training samples as a cyclic generator. Negative samples are generated randomly at
        each step.
        Warning: This generator is endless, make sure you have a stopping condition.

        :param batch_size: The size of a batch to return at each step
        :return: a generation that continuously returns batch of training data.
Get a set of training samples as a cyclic generator. Negative samples are generated randomly at
        each step.
        Warning: This generator is endless, make sure you have a stopping condition.
Get a set of training samples as a cyclic generator . Negative samples are generated randomly at each step . Warning : This generator is endless make sure you have a stopping condition .
def get_training_generator(self, batch_size):
        """
        Get a set of training samples as a cyclic generator. Negative samples are generated randomly at
        each step.
        Warning: This generator is endless, make sure you have a stopping condition.

        :param batch_size: The size of a batch to return at each step
        :return: a generation that continuously returns batch of training data.
        """
        indices = self._get_sample_indices(self._questions_train)
        for i in itertools.cycle(range(0, len(indices), batch_size)):
            batch_indices = indices[i:i + batch_size]
            yield self._get_indexed_samples(batch_indices)
def get_training_generator ( self , batch_size ) : indices = self . _get_sample_indices ( self . _questions_train ) for i in itertools . cycle ( range ( 0 , len ( indices ) , batch_size ) ) : batch_indices = indices [ i : i + batch_size ] yield self . _get_indexed_samples ( batch_indices )