models.py 文件源码

def _negative_sampling(self, num_negative_samples, target_indices):
        assert num_negative_samples > 0

        logging.debug('Stochastically sampling %d negative instances '
                      'out of %d classes (%.2f%%).',
                      num_negative_samples, self.num_entities,
                      100.0 *
                      float(num_negative_samples) / self.num_entities)

        from theano.tensor.shared_randomstreams import RandomStreams

        srng = RandomStreams(
            seed=np.random.randint(low=0, high=(1 << 30)))

        rng_sample_size = (self.batch_size, num_negative_samples,)

        logging.debug(
            'Using %s for random sample generation of %s tensors.',
            RandomStreams, rng_sample_size)

        logging.debug('For every batch %d random integers are sampled.',
                      np.prod(rng_sample_size))

        random_negative_indices = srng.choice(
            rng_sample_size,
            a=self.num_entities,
            p=self.clazz_distribution)

        if self.__DEBUG__:
            random_negative_indices = theano.printing.Print(
                'random_negative_indices')(random_negative_indices)

        return random_negative_indices